Male courtship behaviour - aDN > TNT
Aaron M. Allen
24 February 2020
Setup
library("tidyverse")
library("ggpubr")
library("zoo")
setwd("/mnt/LocalData/behaviour/aDN/aDN_behaviour")
Colours …
gg_color_hue <- function(n) {
hues = seq(15, 375, length = n + 1)
hcl(h = hues, l = 65, c = 100)[1:n]
}gg_color_hue(2)[1] "#F8766D" "#00BFC4"
genotypes <- read_tsv("../2019_03_06_Courtship/genotype.tsv",col_names = TRUE)
genotypes
# indices_list <- list.files("raw data/") %>% str_subset("_Indices.csv")
indices_list <- list.files("../2019_03_06_Courtship/",recursive = TRUE) %>% str_subset("_Indices.csv") %>% str_subset("Male")
all_indices <- tibble()
for (indices_file in indices_list) {
video_name <- indices_file %>% str_remove("/.*")
temp <- read_csv(paste0("../2019_03_06_Courtship/",indices_file))
temp <- temp %>%
mutate(video = video_name)
all_indices <- bind_rows(all_indices,temp)
}
all_indicesall_male_indices <- left_join(x = genotypes,y = all_indices,by=c("video"="video","fly_id"="FlyId"))all_male_indices %>%
group_by(genotype) %>%
summarise(n = length(genotype))unique(all_male_indices$genotype)
[1] "A" "B" "C" "D"
ggplot(all_male_indices,aes(x=genotype,y=CourtshipIndexWithFacing)) +
geom_boxplot()
p1 <- ggplot(all_male_indices,aes(x=genotype,y=CourtshipIndex)) +
geom_boxplot()
p2 <- ggplot(all_male_indices,aes(x=genotype,y=CourtshipIndexWithFacing)) +
geom_boxplot()
p3 <- ggplot(all_male_indices,aes(x=genotype,y=TotalCCI)) +
geom_boxplot()
p4 <- ggplot(all_male_indices,aes(x=genotype,y=TotalCCIwFacing)) +
geom_boxplot()
ggarrange(plotlist = list(p1,p2,p3,p4),
# labels = c("ApproachingIndex",
# "ContactIndex",
# "EncirclingIndex",
# "FacingIndex",
# "TurningIndex",
# "WingIndex"),
# hjust = 1,
ncol = 4,
nrow = 1)
p1 <- ggplot(all_male_indices,aes(x=genotype,y=ApproachingIndex)) +
geom_boxplot()
p2 <- ggplot(all_male_indices,aes(x=genotype,y=ContactIndex)) +
geom_boxplot()
p3 <- ggplot(all_male_indices,aes(x=genotype,y=EncirclingIndex)) +
geom_boxplot()
p4 <- ggplot(all_male_indices,aes(x=genotype,y=FacingIndex)) +
geom_boxplot()
p5 <- ggplot(all_male_indices,aes(x=genotype,y=TurningIndex)) +
geom_boxplot()
p6 <- ggplot(all_male_indices,aes(x=genotype,y=WingIndex)) +
geom_boxplot()
ggarrange(plotlist = list(p1,p2,p3,p4,p5,p6),
# labels = c("ApproachingIndex",
# "ContactIndex",
# "EncirclingIndex",
# "FacingIndex",
# "TurningIndex",
# "WingIndex"),
# hjust = 1,
ncol = 3,
nrow = 2)
p1 <- ggplot(all_male_indices,aes(x=genotype,y=CourtshipInitiation)) +
geom_boxplot()
p2 <- ggplot(all_male_indices,aes(x=genotype,y=CourtshipTermination)) +
geom_boxplot()
p3 <- ggplot(all_male_indices,aes(x=genotype,y=CourtshipDuration)) +
geom_boxplot()
ggarrange(plotlist = list(p1,p2,p3),
ncol = 3,
nrow = 1)
df <- all_male_indices %>%
select(genotype,CourtshipTermination) %>%
group_by(genotype) %>%
mutate(len=length(CourtshipTermination))
#ggplot(df,aes(x=CourtshipTermination,color=genotype)) + geom_step(aes(len=len,y=..y.. * len),stat="ecdf")
ggplot(df,aes(x=CourtshipTermination,color=genotype)) + geom_step(aes(y=..y..),stat="ecdf")
df <- all_male_indices %>%
select(genotype,CourtshipDuration) %>%
group_by(genotype) %>%
mutate(len=length(CourtshipDuration))
#ggplot(df,aes(x=CourtshipDuration,color=genotype)) + geom_step(aes(len=len,y=..y.. * len),stat="ecdf")
ggplot(df,aes(x=CourtshipDuration,color=genotype)) + geom_step(aes(y=..y..),stat="ecdf")
p1 <- ggplot(all_male_indices,aes(x=genotype,y=ApproachingBoutLength)) +
geom_boxplot()
p2 <- ggplot(all_male_indices,aes(x=genotype,y=ContactBoutLength)) +
geom_boxplot()
p3 <- ggplot(all_male_indices,aes(x=genotype,y=EncirclingBoutLength)) +
geom_boxplot()
p4 <- ggplot(all_male_indices,aes(x=genotype,y=FacingBoutLength)) +
geom_boxplot()
p5 <- ggplot(all_male_indices,aes(x=genotype,y=TurningBoutLength)) +
geom_boxplot()
p6 <- ggplot(all_male_indices,aes(x=genotype,y=WingBoutLength)) +
geom_boxplot()
p7 <- ggplot(all_male_indices,aes(x=genotype,y=ApproachingBoutInterval)) +
geom_boxplot()
p8 <- ggplot(all_male_indices,aes(x=genotype,y=ContactBoutInterval)) +
geom_boxplot()
p9 <- ggplot(all_male_indices,aes(x=genotype,y=EncirclingBoutInterval)) +
geom_boxplot()
p10 <- ggplot(all_male_indices,aes(x=genotype,y=FacingBoutInterval)) +
geom_boxplot()
p11 <- ggplot(all_male_indices,aes(x=genotype,y=TurningBoutInterval)) +
geom_boxplot()
p12 <- ggplot(all_male_indices,aes(x=genotype,y=WingBoutInterval)) +
geom_boxplot()
ggarrange(plotlist = list(p1,p2,p3,p4,p5,p6,
p7,p8,p9,p10,p11,p12),
# labels = c("ApproachingIndex",
# "ContactIndex",
# "EncirclingIndex",
# "FacingIndex",
# "TurningIndex",
# "WingIndex"),
# hjust = 1,
ncol = 6,
nrow = 2)
rawdata_list <- list.files("../",recursive = TRUE) %>% str_subset("_ALLDATA.csv") %>% str_subset("_Male_")
all_rawdata <- tibble()
for (rawdata_file in rawdata_list) {
temp <- read_csv(paste0("../",rawdata_file),progress = FALSE)
all_rawdata <- bind_rows(all_rawdata,temp)
}#all_rawdata <- full_join(x = all_rawdata, y = genotypes, by = c("FileName"="video","Id"="fly_id"))
all_rawdata <- all_rawdata %>%
full_join(x = all_rawdata, y = genotypes, by = c("FileName"="video","Id"="fly_id")) %>%
replace_na(list(genotype = "CS female"))
# sum(is.na(all_rawdata$genotype))
# summary(all_rawdata$genotype)
# unique(all_rawdata$genotype)
all_rawdata
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Arena == 2) %>%
filter(Id == 3) %>%
filter(dist_to_other__mm > 2) %>%
ggplot(aes(x=Frame)) +
geom_point(aes(y=wing_l_ang__rad, colour = "#F8766D")) +
geom_point(aes(y=wing_r_ang__rad, colour = "#00BFC4"))
NA
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Arena == 2) %>%
filter(Id == 3) %>%
filter(dist_to_other__mm > 2) %>%
ggplot() +
geom_density(aes(x=wing_l_ang__rad, colour = "#F8766D"),size = 2) +
geom_density(aes(x=wing_r_ang__rad, colour = "#00BFC4"),size = 2)
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Arena == 2) %>%
filter(Id == 3) %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
ggplot() +
geom_density(aes(x=wing_l_ang__rad, colour = "#F8766D"),size = 2) +
geom_density(aes(x=wing_r_ang__rad, colour = "#00BFC4"),size = 2)
# # Extract density data to average
# p <- ggplot_build(test_plot)
# ggplot(as.data.frame(p$data[[1]]), aes(x,y)) + geom_line()
# ggplot(as.data.frame(p$data[[2]]), aes(x,y)) + geom_line()
all_rawdata %>%
filter(genotype == "A") %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
ggplot(aes(x=max_wing_ang__rad,colour=individual)) +
geom_density() + theme(legend.position = "none")
all_rawdata %>%
filter(genotype == "B") %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
ggplot(aes(x=max_wing_ang__rad,colour=individual)) +
geom_density() + theme(legend.position = "none")
all_rawdata %>%
filter(genotype == "C") %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
ggplot(aes(x=max_wing_ang__rad,colour=individual)) +
geom_density() + theme(legend.position = "none")
all_rawdata %>%
filter(genotype == "D") %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
ggplot(aes(x=max_wing_ang__rad,colour=individual)) +
geom_density() + theme(legend.position = "none")
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 3) %>%
ggplot(aes(x=Frame,y=angle_between__rad)) +
geom_point()
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Arena == 2) %>%
ggplot(aes(x=Frame,y=facing_angle__rad, color=as_factor(Id))) +
geom_point()
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Arena == 2) %>%
ggplot() +
geom_density(aes(x=facing_angle__rad, color=as_factor(Id)),size = 2)
all_rawdata %>%
filter(unique_fly == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_6_17") %>%
ggplot(aes(x=Frame,y=dist_to_other__mm)) +
geom_point(aes(colour=c(ifelse(SmoothedDistToOther>0.5,"not copulating","copulating"))))+
geom_point(aes(y=c(ifelse(SmoothedCopulation==1,SmoothedCopulation+6,NA))))+
geom_point(aes(y=c(ifelse(Copulation==1,Copulation+7,NA))))Warning messages:
1: Unknown or uninitialised column: 'bin_max_wing'.
2: Unknown or uninitialised column: 'bin_max_wing'.
3: Unknown or uninitialised column: 'WingGesture'.
4: Unknown or uninitialised column: 'WingGesture'.
5: Unknown or uninitialised column: 'bin_max_wing'.
6: Unknown or uninitialised column: 'bin_max_wing'.
7: Unknown or uninitialised column: 'WingGesture'.
8: Unknown or uninitialised column: 'WingGesture'. 
all_rawdata %>%
filter(unique_fly == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_9_39") %>%
ggplot(aes(x=Frame,y=dist_to_other__mm)) +
geom_point(aes(colour=c(ifelse(SmoothedDistToOther>0.5,"not copulating","copulating"))))+
geom_point(aes(y=c(ifelse(SmoothedCopulation==1,SmoothedCopulation+6,NA))))+
geom_point(aes(y=c(ifelse(Copulation==1,Copulation+7,NA))))
all_rawdata %>%
filter(genotype == "B") %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
ggplot(aes(x=facing_angle__rad,colour=individual)) +
geom_density() + theme(legend.position = "none") +
ylim(0,6)
all_rawdata %>%
filter(genotype == "C") %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
ggplot(aes(x=facing_angle__rad,colour=individual)) +
geom_density() + theme(legend.position = "none") +
ylim(0,6)
all_rawdata %>%
filter(genotype == "D") %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
ggplot(aes(x=facing_angle__rad,colour=individual)) +
geom_density() + theme(legend.position = "none") +
ylim(0,6)
all_rawdata %>%
filter(genotype == "A") %>%
filter(dist_to_other__mm > 2) %>%
#filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
ggplot(aes(x=facing_angle__rad,colour=individual)) +
geom_density() + theme(legend.position = "none") +
ylim(0,6)
all_rawdata %>%
filter(genotype == "D") %>%
filter(dist_to_other__mm > 2) %>%
#filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
ggplot(aes(x=facing_angle__rad,colour=individual)) +
geom_density() + theme(legend.position = "none") +
ylim(0,6)
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Arena == 2) %>%
ggplot(aes(x=Frame,y=dist_to_other__mm, color=dist_to_other__mm)) +
geom_point() 
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 3) %>%
ggplot(aes(x=Frame,y=dist_to_other__mm)) +
geom_point(aes(colour=c(ifelse(dist_to_other__mm>2,"not copulating","copulating"))))
D genotype distance to other
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 27) %>%
ggplot(aes(x=Frame,y=dist_to_other__mm)) +
geom_point(aes(colour=c(ifelse(dist_to_other__mm>2,"not copulating","copulating"))))
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 29) %>%
ggplot(aes(x=Frame,y=dist_to_other__mm)) +
geom_point(aes(colour=c(ifelse(dist_to_other__mm>2,"not copulating","copulating"))))
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 35) %>%
ggplot(aes(x=Frame,y=dist_to_other__mm)) +
geom_point(aes(colour=c(ifelse(dist_to_other__mm>2,"not copulating","copulating"))))
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 37) %>%
ggplot(aes(x=Frame,y=dist_to_other__mm)) +
geom_point(aes(colour=c(ifelse(dist_to_other__mm>2,"not copulating","copulating"))))
all_rawdata <- all_rawdata %>%
unite("unique_fly",FileName,Id, remove = FALSE) %>%
group_by(unique_fly) %>%
mutate(
Multitasking = (Approaching + Encircling + Contact + Turning + WingGesture),
MultitaskingWithFacing = (Approaching + Encircling + Facing + Contact + Turning + WingGesture),
Courtship = ifelse(Multitasking>=1, 1, 0),
CourtshipWithFacing = ifelse(MultitaskingWithFacing>=1, 1, 0),
MultitaskingWithCopulation = (Approaching + Encircling + Contact + Turning + WingGesture + Copulation),
MultitaskingWithCopulationWithFacing = (Approaching + Encircling + Facing + Contact + Turning + WingGesture + Copulation),
CourtshipAndCopulation = ifelse(MultitaskingWithCopulation>=1, 1, 0),
CourtshipAndCopulationWthFacing = ifelse(MultitaskingWithCopulationWithFacing>=1, 1, 0),
SmoothedCourtship = ifelse((rollmean(Courtship, 150, fill = c(0,0,0), align = c("left")))>0.5, 1, 0),
SmoothedCopulation = ifelse((rollmean(Copulation, 1250, fill = c(0,0,0), align = c("center")))>0.5, 1, 0),
SmoothedDistToOther = ifelse((rollmean(ifelse(dist_to_other__mm > 2, 1, 0), 250, fill = c(1,1,NA), align = c("center")))>0.5, 1, 0)
)
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 37) %>%
ggplot(aes(x=Frame,y=dist_to_other__mm)) +
geom_point(aes(colour=c(ifelse(SmoothedDistToOther==1,"not copulating","copulating"))))
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 37) %>%
ggplot(aes(x=Frame)) +
geom_point(aes(y=SmoothedDistToOther, colour = "#F8766D")) +
geom_point(aes(y=(SmoothedCopulation+0.1), colour= "#00BFC4"))
NA
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 35) %>%
ggplot(aes(x=Frame,y=dist_to_other__mm)) +
geom_point(aes(colour=c(ifelse(SmoothedDistToOther==1,"not copulating","copulating"))))
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 35) %>%
ggplot(aes(x=Frame)) +
geom_point(aes(y=SmoothedDistToOther, colour = "#F8766D")) +
geom_point(aes(y=(SmoothedCopulation+0.1), colour= "#00BFC4"))
NA
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 35) %>%
ggplot(aes(x=Frame)) +
geom_point(aes(y=SmoothedDistToOther, colour = "#F8766D")) +
geom_point(aes(y=(SmoothedCopulation+0.1), colour= "#00BFC4")) +
xlim(2000,3000)
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 35) %>%
summarise(which.min(SmoothedDistToOther),
which.max(SmoothedCopulation))
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 3) %>%
ggplot(aes(x=Frame,y=dist_to_other__mm)) +
geom_point(aes(colour=c(ifelse(SmoothedDistToOther==1,"not copulating","copulating"))))
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 3) %>%
ggplot(aes(x=Frame,y=dist_to_other__mm)) +
geom_point(aes(colour=c(ifelse(SmoothedCopulation==0,"not copulating","copulating"))))
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 3) %>%
ggplot(aes(x=Frame)) +
geom_point(aes(y=SmoothedDistToOther, colour = "#F8766D")) +
geom_point(aes(y=(SmoothedCopulation+0.1), colour= "#00BFC4")) +
xlim(6000,7000)
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 3) %>%
summarise(which.min(SmoothedDistToOther),
which.max(SmoothedCopulation))
D genotype distance to other
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 27) %>%
ggplot(aes(x=Frame,y=dist_to_other__mm)) +
geom_point(aes(colour=c(ifelse(SmoothedDistToOther==1,"not copulating","copulating"))))
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 29) %>%
ggplot(aes(x=Frame,y=dist_to_other__mm)) +
geom_point(aes(colour=c(ifelse(SmoothedDistToOther==1,"not copulating","copulating"))))
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 35) %>%
ggplot(aes(x=Frame,y=dist_to_other__mm)) +
geom_point(aes(colour=c(ifelse(SmoothedDistToOther>0.5,"not copulating","copulating"))))
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 37) %>%
ggplot(aes(x=Frame,y=dist_to_other__mm)) +
geom_point(aes(colour=c(ifelse(SmoothedDistToOther==1,"not copulating","copulating"))))
all_rawdata %>%
filter(genotype == "D") %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
ggplot(aes(x=max_wing_ang__rad,colour=individual)) +
geom_density() + theme(legend.position = "none") +
ylim(0,6) +
xlim(0,pi)
all_rawdata %>%
filter(genotype == "A") %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
ggplot(aes(x=angle_between__rad,colour=individual)) +
geom_density() + theme(legend.position = "none") +
ylim(0,3) +
xlim(0,pi)
all_rawdata %>%
filter(genotype == "D") %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
ggplot(aes(x=angle_between__rad,colour=individual)) +
geom_density() + theme(legend.position = "none") +
ylim(0,3) +
xlim(0,pi)
Distance to wall
all_rawdata %>%
filter(genotype == "A") %>%
filter(dist_to_other__mm > 2) %>%
#filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
ggplot(aes(x=dist_to_wall__mm,colour=individual)) +
geom_density() + theme(legend.position = "none") +
ylim(0,2.2) +
xlim(0,10)
all_rawdata %>%
filter(genotype == "D") %>%
filter(dist_to_other__mm > 2) %>%
#filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
ggplot(aes(x=dist_to_wall__mm,colour=individual)) +
geom_density() + theme(legend.position = "none") +
ylim(0,2.2) +
xlim(0,10)
p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
mean_dist_to_wall_mm = mean(dist_to_wall__mm[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)),
which.max(SmoothedCopulation),
(which.max(SmoothedCourtship)+(25*600))),
(which.max(SmoothedCourtship)+(25*600))
)
]
)
) %>%
ggplot(aes(x=genotype,y=mean_dist_to_wall_mm)) +
geom_boxplot()
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
median_dist_to_wall_mm = median(dist_to_wall__mm[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)),
which.max(SmoothedCopulation),
(which.max(SmoothedCourtship)+(25*600))),
(which.max(SmoothedCourtship)+(25*600))
)
]
)
) %>%
ggplot(aes(x=genotype,y=median_dist_to_wall_mm)) +
geom_boxplot()
p3 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
prop_dist_to_wall_lt_1 = 100*sum(dist_to_wall__mm[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)),
which.max(SmoothedCopulation),
(which.max(SmoothedCourtship)+(25*600))),
(which.max(SmoothedCourtship)+(25*600))
)
]<1,
na.rm = TRUE)/
length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)),
which.max(SmoothedCopulation),
(which.max(SmoothedCourtship)+(25*600))),
(which.max(SmoothedCourtship)+(25*600))
)
]
)) %>%
ggplot(aes(x=genotype,y=prop_dist_to_wall_lt_1)) +
geom_boxplot()
ggarrange(plotlist = list(p1,p2,p3),
labels = c("Mean Distance to Wall",
"Median Distance to Wall",
"Prop Time Close to Wall"),
hjust = -0.2,
ncol = 3,
nrow = 1)
all_rawdata %>%
filter(genotype != "CS female") %>%
filter(dist_to_other__mm > 2) %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
ggplot(aes(x=pos_x__px,y=pos_y__px)) +
geom_point()
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 36) %>%
filter(dist_to_other__mm > 2) %>%
ggplot(aes(x=pos_x__px,y=pos_y__px)) +
geom_point() +
coord_fixed()
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 36) %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_wall__mm > 1) %>%
ggplot(aes(x=pos_x__px,y=pos_y__px)) +
geom_point() +
coord_fixed()
p1 <- all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 36) %>%
#filter(dist_to_other__mm > 2) %>%
#filter(dist_to_wall__mm > 1) %>%
ggplot(aes(x=genotype,y=dist_to_wall__mm)) +
geom_boxplot() +
ylim(0,10)
p2 <- all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 36) %>%
filter(dist_to_other__mm > 2) %>%
#filter(dist_to_wall__mm > 1) %>%
ggplot(aes(x=genotype,y=dist_to_wall__mm)) +
geom_boxplot() +
ylim(0,10)
p3 <- all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 36) %>%
#filter(dist_to_other__mm > 2) %>%
filter(dist_to_wall__mm > 1) %>%
ggplot(aes(x=genotype,y=dist_to_wall__mm)) +
geom_boxplot() +
ylim(0,10)
p4 <- all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 36) %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_wall__mm > 1) %>%
ggplot(aes(x=genotype,y=dist_to_wall__mm)) +
geom_boxplot() +
ylim(0,10)
ggarrange(plotlist = list(p1,p2,p3,p4),
labels = c("all frames",
"non cop frames",
"gt 1mm from wall",
"non cop, gt 1mm"),
hjust = -0.4,
ncol = 4,
nrow = 1)
all_rawdata %>%
filter(genotype == "A") %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
ggplot(aes(x=dist_to_other__mm,colour=individual)) +
geom_density() + theme(legend.position = "none") +
ylim(0,2) +
xlim(0,20)
all_rawdata %>%
filter(genotype == "B") %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
ggplot(aes(x=dist_to_other__mm,colour=individual)) +
geom_density() + theme(legend.position = "none") +
ylim(0,2) +
xlim(0,20)
all_rawdata %>%
filter(genotype == "C") %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
ggplot(aes(x=dist_to_other__mm,colour=individual)) +
geom_density() + theme(legend.position = "none") +
ylim(0,2) +
xlim(0,20)
all_rawdata %>%
filter(genotype == "D") %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
ggplot(aes(x=dist_to_other__mm,colour=individual)) +
geom_density() + theme(legend.position = "none") +
ylim(0,2) +
xlim(0,20)
all_rawdata %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
summarise(mean_dist_to_other = mean(dist_to_other__mm))
mean distance to other
p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
#filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(mean_dist_to_other = mean(dist_to_other__mm),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=mean_dist_to_other)) +
geom_boxplot() +
ylim(0,10)
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(mean_dist_to_other = mean(dist_to_other__mm),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=mean_dist_to_other)) +
geom_boxplot() +
ylim(0,10)
p3 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad < (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(mean_dist_to_other = mean(dist_to_other__mm),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=mean_dist_to_other)) +
geom_boxplot() +
ylim(0,10)
ggarrange(plotlist = list(p1,p2,p3),
labels = c("All Frames","Wing Extension Frames","Non-Wing Extension Frames"),
#hjust = 1,
ncol = 3,
nrow = 1)
median distance to other
p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
#filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(median_dist_to_other = median(dist_to_other__mm),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=median_dist_to_other)) +
geom_boxplot() +
ylim(0,10)
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(median_dist_to_other = median(dist_to_other__mm),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=median_dist_to_other)) +
geom_boxplot() +
ylim(0,10)
p3 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad < (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(median_dist_to_other = median(dist_to_other__mm),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=median_dist_to_other)) +
geom_boxplot() +
ylim(0,10)
ggarrange(plotlist = list(p1,p2,p3),
labels = c("All Frames","Wing Extension Frames","Non-Wing Extension Frames"),
hjust = -0.4,
ncol = 3,
nrow = 1)
mean facing angle
p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
#filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(mean_facing_angle = mean(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=mean_facing_angle)) +
geom_boxplot() +
ylim(0,pi)
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(mean_facing_angle = mean(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=mean_facing_angle)) +
geom_boxplot() +
ylim(0,pi)
p3 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad < (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(mean_facing_angle = mean(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=mean_facing_angle)) +
geom_boxplot() +
ylim(0,pi)
ggarrange(plotlist = list(p1,p2,p3),
labels = c("All Frames","Wing Extension Frames","Non-Wing Extension Frames"),
#hjust = 1,
ncol = 3,
nrow = 1)
median facing angle
p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
#filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(median_facing_angle = median(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=median_facing_angle)) +
geom_boxplot() +
ylim(0,pi)
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(median_facing_angle = median(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=median_facing_angle)) +
geom_boxplot() +
ylim(0,pi)
p3 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad < (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(median_facing_angle = median(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=median_facing_angle)) +
geom_boxplot() +
ylim(0,pi)
ggarrange(plotlist = list(p1,p2,p3),
labels = c("All Frames","Wing Extension Frames","Non-Wing Extension Frames"),
#hjust = 1,
ncol = 3,
nrow = 1)
p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
#filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(median_facing_angle = median(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=median_facing_angle)) +
geom_boxplot() +
ylim(0,1.5)
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(median_facing_angle = median(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=median_facing_angle)) +
geom_boxplot() +
ylim(0,1)
p3 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad < (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(median_facing_angle = median(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=median_facing_angle)) +
geom_boxplot() +
ylim(0,2)
ggarrange(plotlist = list(p1,p2,p3),
labels = c("All Frames","Wing Extension Frames","Non-Wing Extension Frames"),
#hjust = 1,
ncol = 3,
nrow = 1)
test_stat_tibble0 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
#filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(median_facing_angle = median(facing_angle__rad),
genotype = unique(genotype))aov0 <- aov(median_facing_angle~genotype,data = test_stat_tibble0)
summary(aov0) Df Sum Sq Mean Sq F value Pr(>F)
genotype 3 1.036 0.3453 2.736 0.0468 *
Residuals 114 14.388 0.1262
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1TukeyHSD(aov0) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = median_facing_angle ~ genotype, data = test_stat_tibble0)
$genotype
diff lwr upr p adj
B-A -0.07051500 -0.3361762470 0.1951462 0.8999385
C-A 0.04970748 -0.1803176760 0.2797326 0.9426712
D-A 0.19276677 -0.0389146073 0.4244482 0.1380793
C-B 0.12022248 -0.1408020849 0.3812470 0.6275505
D-B 0.26328177 0.0007965104 0.5257670 0.0490063
D-C 0.14305929 -0.0832904123 0.3694090 0.3562046
median facing angle by sector
p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad < pi/6) %>%
#filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(median_facing_angle = median(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=median_facing_angle)) +
geom_boxplot() +
ylim(0,pi/6)
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad < pi/6) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(median_facing_angle = median(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=median_facing_angle)) +
geom_boxplot() +
ylim(0,pi/6)
p3 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad < pi/6) %>%
filter(max_wing_ang__rad < (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(median_facing_angle = median(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=median_facing_angle)) +
geom_boxplot() +
ylim(0,pi/6)
p4 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad > pi/6) %>%
#filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(median_facing_angle = median(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=median_facing_angle)) +
geom_boxplot() +
ylim(0,pi)
p5 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad > pi/6) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(median_facing_angle = median(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=median_facing_angle)) +
geom_boxplot() +
ylim(0,pi)
p6 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad > pi/6) %>%
filter(max_wing_ang__rad < (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(median_facing_angle = median(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=median_facing_angle)) +
geom_boxplot() +
ylim(0,pi)
ggarrange(plotlist = list(p1,p2,p3,p4,p5,p6),
labels = c("All Frames","Wing Extension Frames","Non-Wing Extension Frames"),
#hjust = 1,
ncol = 3,
nrow = 2)
mean facing angle by sector
p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad < pi/6) %>%
#filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(mean_facing_angle = mean(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=mean_facing_angle)) +
geom_boxplot() +
ylim(0,pi/6)
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad < pi/6) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(mean_facing_angle = mean(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=mean_facing_angle)) +
geom_boxplot() +
ylim(0,pi/6)
p3 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad < pi/6) %>%
filter(max_wing_ang__rad < (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(mean_facing_angle = mean(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=mean_facing_angle)) +
geom_boxplot() +
ylim(0,pi/6)
p4 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad > pi/6) %>%
#filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(mean_facing_angle = mean(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=mean_facing_angle)) +
geom_boxplot() +
ylim(0,pi)
p5 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad > pi/6) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(mean_facing_angle = mean(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=mean_facing_angle)) +
geom_boxplot() +
ylim(0,pi)
p6 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad > pi/6) %>%
filter(max_wing_ang__rad < (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(mean_facing_angle = mean(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=mean_facing_angle)) +
geom_boxplot() +
ylim(0,pi)
ggarrange(plotlist = list(p1,p2,p3,p4,p5,p6),
labels = c("All Frames","Wing Extension Frames","Non-Wing Extension Frames"),
#hjust = 1,
ncol = 3,
nrow = 2)
mean facing angle by wee sectors
p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad > 0) %>%
filter(facing_angle__rad <= pi/6) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(mean_facing_angle = mean(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=mean_facing_angle)) +
geom_boxplot() #+
#ylim(0,pi/6)
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad > pi/6) %>%
filter(facing_angle__rad <= 2*pi/6) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(mean_facing_angle = mean(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=mean_facing_angle)) +
geom_boxplot() #+
#ylim(0,pi/6)
p3 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad > 2*pi/6) %>%
filter(facing_angle__rad <= 3*pi/6) %>%
filter(max_wing_ang__rad < (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(mean_facing_angle = mean(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=mean_facing_angle)) +
geom_boxplot() #+
#ylim(0,pi/6)
p4 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad > 3*pi/6) %>%
filter(facing_angle__rad <= 4*pi/6) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(mean_facing_angle = mean(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=mean_facing_angle)) +
geom_boxplot() #+
#ylim(0,pi)
p5 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad > 4*pi/6) %>%
filter(facing_angle__rad <= 5*pi/6) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(mean_facing_angle = mean(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=mean_facing_angle)) +
geom_boxplot() #+
#ylim(0,pi)
p6 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad > 5*pi/6) %>%
filter(facing_angle__rad <= 6*pi/6) %>%
filter(max_wing_ang__rad < (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(mean_facing_angle = mean(facing_angle__rad),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=mean_facing_angle)) +
geom_boxplot() #+
#ylim(0,pi)
ggarrange(plotlist = list(p1,p2,p3,p4,p5,p6),
labels = c("0-pi/6","pi/6-2pi/6","2pi/6-3pi/6",
"3pi/6-4pi/6","4pi/6-5pi/6","5pi/6-6pi/6"),
#hjust = 1,
ncol = 3,
nrow = 2)
counts in wee sectors
p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad > 0) %>%
filter(facing_angle__rad <= pi/6) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(counts = length(Frame),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=counts)) +
geom_boxplot() #+
#ylim(0,pi/6)
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad > pi/6) %>%
filter(facing_angle__rad <= 2*pi/6) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(counts = length(Frame),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=counts)) +
geom_boxplot() #+
#ylim(0,pi/6)
p3 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad > 2*pi/6) %>%
filter(facing_angle__rad <= 3*pi/6) %>%
filter(max_wing_ang__rad < (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(counts = length(Frame),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=counts)) +
geom_boxplot() #+
#ylim(0,pi/6)
p4 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad > 3*pi/6) %>%
filter(facing_angle__rad <= 4*pi/6) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(counts = length(Frame),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=counts)) +
geom_boxplot() #+
#ylim(0,pi)
p5 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad > 4*pi/6) %>%
filter(facing_angle__rad <= 5*pi/6) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(counts = length(Frame),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=counts)) +
geom_boxplot() #+
#ylim(0,pi)
p6 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad > 5*pi/6) %>%
filter(facing_angle__rad <= 6*pi/6) %>%
filter(max_wing_ang__rad < (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(counts = length(Frame),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=counts)) +
geom_boxplot() #+
#ylim(0,pi)
ggarrange(plotlist = list(p1,p2,p3,p4,p5,p6),
labels = c("0-pi/6","pi/6-2pi/6","2pi/6-3pi/6",
"3pi/6-4pi/6","4pi/6-5pi/6","5pi/6-6pi/6"),
#hjust = 1,
ncol = 3,
nrow = 2)
Total time in sector
p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad > 0) %>%
filter(facing_angle__rad <= pi/8) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(counts = length(Frame),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=counts)) +
geom_boxplot() #+
#ylim(0,pi/6)
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(facing_angle__rad > pi/8) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(counts = length(Frame),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=counts)) +
geom_boxplot() #+
#ylim(0,pi/6)
ggarrange(plotlist = list(p1,p2),
labels = c("<pi/8",">pi/8"),
hjust = -0.6,
vjust = 2,
ncol = 2,
nrow = 1)
Proportion time in sector
p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(counts = 100*sum(facing_angle__rad <= pi/8)/length(Frame),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=counts)) +
geom_boxplot() #+
#ylim(0,pi/6)
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(counts = 100*sum(facing_angle__rad > pi/8)/length(Frame),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=counts)) +
geom_boxplot() #+
#ylim(0,pi/6)
ggarrange(plotlist = list(p1,p2),
labels = c("<pi/8",">pi/8"),
hjust = -1.4,
vjust = 2,
ncol = 2,
nrow = 1)
test_stat_tibble0 <- all_rawdata %>%
filter(genotype != "CS female") %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
group_by(unique_fly) %>%
summarise(counts = 100*sum(facing_angle__rad > pi/8)/length(Frame),
genotype = unique(genotype))
aov0 <- aov(counts~genotype,data = test_stat_tibble0)
#summary(aov0)
TukeyHSD(aov0) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = counts ~ genotype, data = test_stat_tibble0)
$genotype
diff lwr upr p adj
B-A 6.6367127 -2.5650354 15.838461 0.2421768
C-A 7.3146600 -0.6527555 15.282076 0.0840396
D-A 16.6152204 8.5904380 24.640003 0.0000022
C-B 0.6779474 -8.3631993 9.719094 0.9973376
D-B 9.9785077 0.8867666 19.070249 0.0254776
D-C 9.3005603 1.4604520 17.140669 0.0131217
pairwise.t.test(test_stat_tibble0$counts, test_stat_tibble0$genotype)
Pairwise comparisons using t tests with pooled SD
data: test_stat_tibble0$counts and test_stat_tibble0$genotype
A B C
B 0.125 - -
C 0.055 0.845 -
D 2.2e-06 0.020 0.012
P value adjustment method: holm
p1 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "A") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
geom_bin2d(bins = c(200,50)) +
xlim(0,2*pi) +
ylim(0,20) +
scale_fill_continuous(type = "viridis",limits = c(2,2000),na.value = "white") +
coord_polar() +
theme_void()
p2 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "B") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
geom_bin2d(bins = c(200,50)) +
xlim(0,2*pi) +
ylim(0,20) +
scale_fill_continuous(type = "viridis",limits = c(2,2000),na.value = "white") +
coord_polar() +
theme_void()
p3 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "C") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
geom_bin2d(bins = c(200,50)) +
xlim(0,2*pi) +
ylim(0,20) +
scale_fill_continuous(type = "viridis",limits = c(2,2000),na.value = "white") +
coord_polar() +
theme_void()
p4 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "D") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
geom_bin2d(bins = c(200,50)) +
xlim(0,2*pi) +
ylim(0,20) +
scale_fill_continuous(type = "viridis",limits = c(2,2000),na.value = "white") +
coord_polar() +
theme_void()
ggarrange(plotlist = list(p1,p2,p3,p4),
labels = c("A","B","C","D"),
#hjust = 1,
ncol = 2,
nrow = 2)
Total time in sector
p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(facing_angle__rad > 0) %>%
filter(facing_angle__rad <= pi/8) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(counts = length(Frame),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=counts)) +
geom_boxplot() #+
#ylim(0,pi/6)
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(facing_angle__rad > pi/8) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(counts = length(Frame),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=counts)) +
geom_boxplot() #+
#ylim(0,pi/6)
ggarrange(plotlist = list(p1,p2),
labels = c("<pi/8",">pi/8"),
hjust = -0.6,
vjust = 2,
ncol = 2,
nrow = 1)
Proportion time in sector and within 10mm
p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(counts = 100*sum(facing_angle__rad <= pi/8)/length(Frame),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=counts)) +
geom_boxplot() #+
#ylim(0,pi/6)
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(counts = 100*sum(facing_angle__rad > pi/8)/length(Frame),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=counts)) +
geom_boxplot() #+
#ylim(0,pi/6)
ggarrange(plotlist = list(p1,p2),
labels = c("<pi/8",">pi/8"),
hjust = -1.4,
vjust = 2,
ncol = 2,
nrow = 1)
test_stat_tibble0 <- all_rawdata %>%
filter(genotype != "CS female") %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
group_by(unique_fly) %>%
summarise(counts = 100*sum(facing_angle__rad > pi/8)/length(Frame),
genotype = unique(genotype))
aov0 <- aov(counts~genotype,data = test_stat_tibble0)
#summary(aov0)
TukeyHSD(aov0) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = counts ~ genotype, data = test_stat_tibble0)
$genotype
diff lwr upr p adj
B-A 4.2169183 -3.2364908 11.670328 0.4558215
C-A 4.8104624 -1.6431383 11.264063 0.2159140
D-A 11.2681432 4.7680753 17.768211 0.0000889
C-B 0.5935441 -6.7297782 7.916866 0.9966463
D-B 7.0512248 -0.3130788 14.415528 0.0658244
D-C 6.4576807 0.1071987 12.808163 0.0446836
pairwise.t.test(test_stat_tibble0$counts, test_stat_tibble0$genotype)
Pairwise comparisons using t tests with pooled SD
data: test_stat_tibble0$counts and test_stat_tibble0$genotype
A B C
B 0.286 - -
C 0.163 0.833 -
D 9.1e-05 0.056 0.046
P value adjustment method: holm
p1 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "A") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
geom_bin2d(bins = c(200,50)) +
xlim(0,2*pi) +
ylim(0,10) +
scale_fill_continuous(type = "viridis",limits = c(0,2000),na.value = "white") +
theme_void() +
coord_polar()
p2 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "B") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
geom_bin2d(bins = c(200,50)) +
xlim(0,2*pi) +
ylim(0,10) +
scale_fill_continuous(type = "viridis",limits = c(0,2000),na.value = "white") +
theme_void() +
coord_polar()
p3 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "C") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
geom_bin2d(bins = c(200,50)) +
xlim(0,2*pi) +
ylim(0,10) +
scale_fill_continuous(type = "viridis",limits = c(0,2000),na.value = "white") +
theme_void() +
coord_polar()
p4 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "D") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
geom_bin2d(bins = c(200,50)) +
xlim(0,2*pi) +
ylim(0,10) +
scale_fill_continuous(type = "viridis",limits = c(0,2000),na.value = "white") +
theme_void() +
coord_polar()
ggarrange(plotlist = list(p1,p2,p3,p4),
labels = c("A","B","C","D"),
#hjust = 1,
ncol = 2,
nrow = 2)
p1 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "A") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
geom_bin2d(bins = c(200,50)) +
xlim(0,2*pi) +
ylim(0,10) +
scale_fill_continuous(type = "viridis",limits = c(4,2000),na.value = "white") +
theme_void() +
coord_polar()
p2 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "B") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
geom_bin2d(bins = c(200,50)) +
xlim(0,2*pi) +
ylim(0,10) +
scale_fill_continuous(type = "viridis",limits = c(4,2000),na.value = "white") +
theme_void() +
coord_polar()
p3 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "C") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
geom_bin2d(bins = c(200,50)) +
xlim(0,2*pi) +
ylim(0,10) +
scale_fill_continuous(type = "viridis",limits = c(4,2000),na.value = "white") +
theme_void() +
coord_polar()
p4 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "D") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
geom_bin2d(bins = c(200,50)) +
xlim(0,2*pi) +
ylim(0,10) +
scale_fill_continuous(type = "viridis",limits = c(4,2000),na.value = "white") +
theme_void() +
coord_polar()
ggarrange(plotlist = list(p1,p2,p3,p4),
labels = c("A","B","C","D"),
#hjust = 1,
ncol = 2,
nrow = 2)
p1 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "A") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
geom_bin2d(bins = c(40,10)) +
xlim(0,2*pi) +
ylim(0,10) +
scale_fill_continuous(type = "viridis",limits = c(50,50000),na.value = "white") +
theme_void() +
coord_polar()
p2 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "B") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
geom_bin2d(bins = c(40,10)) +
xlim(0,2*pi) +
ylim(0,10) +
scale_fill_continuous(type = "viridis",limits = c(50,50000),na.value = "white") +
theme_void() +
coord_polar()
p3 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "C") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
geom_bin2d(bins = c(40,10)) +
xlim(0,2*pi) +
ylim(0,10) +
scale_fill_continuous(type = "viridis",limits = c(50,50000),na.value = "white") +
theme_void() +
coord_polar()
p4 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "D") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
geom_bin2d(bins = c(40,10)) +
xlim(0,2*pi) +
ylim(0,10) +
scale_fill_continuous(type = "viridis",limits = c(50,50000),na.value = "white") +
theme_void() +
coord_polar()
ggarrange(plotlist = list(p1,p2,p3,p4),
labels = c("A","B","C","D"),
#hjust = 1,
ncol = 2,
nrow = 2)
p1 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "A") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
geom_bin2d(bins = c(100,25)) +
xlim(0,2*pi) +
ylim(0,10) +
scale_fill_continuous(type = "viridis",limits = c(10,2000),na.value = "white") +
theme_void() +
coord_polar()
p2 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "B") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
geom_bin2d(bins = c(100,25)) +
xlim(0,2*pi) +
ylim(0,10) +
scale_fill_continuous(type = "viridis",limits = c(10,2000),na.value = "white") +
theme_void() +
coord_polar()
p3 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "C") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
geom_bin2d(bins = c(100,25)) +
xlim(0,2*pi) +
ylim(0,10) +
scale_fill_continuous(type = "viridis",limits = c(10,2000),na.value = "white") +
theme_void() +
coord_polar()
p4 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "D") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
geom_bin2d(bins = c(100,25)) +
xlim(0,2*pi) +
ylim(0,10) +
scale_fill_continuous(type = "viridis",limits = c(10,2000),na.value = "white") +
theme_void() +
coord_polar()
ggarrange(plotlist = list(p1,p2,p3,p4),
labels = c("A","B","C","D"),
#hjust = 1,
ncol = 2,
nrow = 2)
p1 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "A") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
stat_density_2d(aes(fill = ..level..), geom = "polygon", colour="white") +
xlim(0,2*pi) +
ylim(0,6) +
#scale_fill_continuous(type = "viridis",limits = c(2,2000),na.value = "white") +
theme_grey() +
coord_polar()
p2 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "B") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
stat_density_2d(aes(fill = ..level..), geom = "polygon", colour="white") +
xlim(0,2*pi) +
ylim(0,6) +
#scale_fill_continuous(type = "viridis",limits = c(2,2000),na.value = "white") +
theme_grey() +
coord_polar()
p3 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "C") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
stat_density_2d(aes(fill = ..level..), geom = "polygon", colour="white") +
xlim(0,2*pi) +
ylim(0,6) +
#scale_fill_continuous(type = "viridis",limits = c(2,2000),na.value = "white") +
theme_grey() +
coord_polar()
p4 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
filter(genotype == "D") %>%
ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
stat_density_2d(aes(fill = ..level..), geom = "polygon", colour="white") +
xlim(0,2*pi) +
ylim(0,6) +
#scale_fill_continuous(type = "viridis",limits = c(2,2000),na.value = "white") +
theme_grey() +
coord_polar()
ggarrange(plotlist = list(p1,p2,p3,p4),
labels = c("A","B","C","D"),
#hjust = 1,
ncol = 2,
nrow = 2)
Total count in inner ring
p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(facing_angle__rad <= pi/8) %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm <= 3) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(counts = length(Frame),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=counts)) +
geom_boxplot() #+
#ylim(0,pi/6)
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(facing_angle__rad <= pi/8) %>%
filter(dist_to_other__mm > 3) %>%
filter(dist_to_other__mm <= 10) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(counts = length(Frame),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=counts)) +
geom_boxplot() #+
#ylim(0,pi/6)
ggarrange(plotlist = list(p1,p2),
labels = c("2mm-3mm","3mm-10mm"),
hjust = -0.6,
vjust = 2,
ncol = 2,
nrow = 1)
Proportion count in inner ring
p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(facing_angle__rad <= pi/8) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(counts = 100*sum(dist_to_other__mm > 2 & dist_to_other__mm <= 3)/length(Frame),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=counts)) +
geom_boxplot() #+
#ylim(0,pi/6)
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
filter(facing_angle__rad <= pi/8) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
unite("individual", FileName:Id, remove = FALSE) %>%
group_by(individual) %>%
summarise(counts = 100*sum(dist_to_other__mm > 3 & dist_to_other__mm <= 10)/length(Frame),
genotype = unique(genotype)) %>%
ggplot(aes(x=genotype, y=counts)) +
geom_boxplot() #+
#ylim(0,pi/6)
ggarrange(plotlist = list(p1,p2),
labels = c("2mm-3mm","3mm-10mm"),
hjust = -0.6,
vjust = 2,
ncol = 2,
nrow = 1)
test_stat_tibble0 <- all_rawdata %>%
filter(genotype != "CS female") %>%
filter(facing_angle__rad <= pi/8) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
group_by(unique_fly) %>%
summarise(counts = 100*sum(dist_to_other__mm > 2 & dist_to_other__mm <= 3)/length(Frame),
genotype = unique(genotype))
aov0 <- aov(counts~genotype,data = test_stat_tibble0)
#summary(aov0)
TukeyHSD(aov0) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = counts ~ genotype, data = test_stat_tibble0)
$genotype
diff lwr upr p adj
B-A 2.7255382 -8.488600 13.93968 0.9209524
C-A 2.5367905 -7.173071 12.24665 0.9040570
D-A 21.8215793 12.041805 31.60135 0.0000003
C-B -0.1887477 -11.207161 10.82967 0.9999678
D-B 19.0960411 8.015968 30.17611 0.0000986
D-C 19.2847888 9.730076 28.83950 0.0000040
Left vs Right wing
p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
wing_index = 100*sum(WingGesture[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)),
which.max(SmoothedCopulation),
(which.max(SmoothedCourtship)+(25*600))),
(which.max(SmoothedCourtship)+(25*600))
)
]
)/
length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)),
which.max(SmoothedCopulation),
(which.max(SmoothedCourtship)+(25*600))),
(which.max(SmoothedCourtship)+(25*600))
)
]
)) %>%
ggplot(aes(x=genotype,y=wing_index)) +
geom_boxplot()
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
wing_index = 100*sum(max_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)),
which.max(SmoothedCopulation),
(which.max(SmoothedCourtship)+(25*600))),
(which.max(SmoothedCourtship)+(25*600))
)
]>(35*pi/180),
na.rm = TRUE)/
length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)),
which.max(SmoothedCopulation),
(which.max(SmoothedCourtship)+(25*600))),
(which.max(SmoothedCourtship)+(25*600))
)
]
)) %>%
ggplot(aes(x=genotype,y=wing_index)) +
geom_boxplot()
ggarrange(plotlist = list(p1,p2),
labels = c("JAABA","wing angle"),
hjust = -0.6,
vjust = 2,
ncol = 2,
nrow = 1)
all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
Wing_index_by_JAABA = 100*sum(WingGesture[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]
)/
length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]
),
Wing_index_by_angle = 100*sum(max_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]>(35*pi/180),
na.rm = TRUE)/
length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]
)) %>%
ggplot(aes(x=Wing_index_by_angle,y=Wing_index_by_JAABA,colour=genotype)) +
geom_point() +
geom_smooth(method=lm) +
stat_cor(label.y = c(52,56,60,64), size =5) +
stat_regline_equation(label.y = c(50,54,58,62), size =5) +
coord_fixed()
wind <- 600
p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
wing_index = 100*sum(max_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]>(35*pi/180),
na.rm = TRUE)/
length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]
)) %>%
ggplot(aes(x=genotype,y=wing_index)) +
geom_boxplot()
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
left_wing_index = 100*sum(wing_l_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]<(-35*pi/180),
na.rm = TRUE)/
length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]
)) %>%
ggplot(aes(x=genotype,y=left_wing_index)) +
geom_boxplot()
p3 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
right_wing_index = 100*sum(wing_r_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]>(35*pi/180),
na.rm = TRUE)/
length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]
)) %>%
ggplot(aes(x=genotype,y=right_wing_index)) +
geom_boxplot()
p4 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
both_wing_index = 100*sum(min_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]>(35*pi/180),
na.rm = TRUE)/
length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]
)) %>%
ggplot(aes(x=genotype,y=both_wing_index)) +
geom_boxplot()
ggarrange(plotlist = list(p1,p4,p2,p3),
labels = c("either wing","both wings","left wing","right wing"),
hjust = -0.6,
vjust = 2,
ncol = 2,
nrow = 2)
test_stat_tibble <- all_rawdata %>%
filter(genotype != "CS female") %>%
#filter(genotype != "A") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
both_wing_index = 100*sum(min_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]>(35*pi/180),
na.rm = TRUE)/
length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]
))
aov1 <- aov(both_wing_index~genotype, data = test_stat_tibble)
TukeyHSD(aov1) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = both_wing_index ~ genotype, data = test_stat_tibble)
$genotype
diff lwr upr p adj
B-A -0.4779248 -2.0714700 1.115620 0.8624926
C-A -0.1148557 -1.4946410 1.264930 0.9963700
D-A 0.8787432 -0.5109769 2.268463 0.3557950
C-B 0.3630691 -1.2026635 1.928802 0.9304240
D-B 1.3566679 -0.2178265 2.931162 0.1170595
D-C 0.9935989 -0.3641396 2.351337 0.2304436
wind <- 600
p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
both_wing_index = 100*sum(min_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]>(10*pi/180),
na.rm = TRUE)/
length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]
)) %>%
ggplot(aes(x=genotype,y=both_wing_index)) +
geom_boxplot()
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
both_wing_index = 100*sum(min_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]>(20*pi/180),
na.rm = TRUE)/
length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]
)) %>%
ggplot(aes(x=genotype,y=both_wing_index)) +
geom_boxplot()
p3 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
both_wing_index = 100*sum(min_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]>(30*pi/180),
na.rm = TRUE)/
length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]
)) %>%
ggplot(aes(x=genotype,y=both_wing_index)) +
geom_boxplot()
p4 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
both_wing_index = 100*sum(min_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]>(40*pi/180),
na.rm = TRUE)/
length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]
)) %>%
ggplot(aes(x=genotype,y=both_wing_index)) +
geom_boxplot()
ggarrange(plotlist = list(p1,p2,p3,p4),
labels = c(">10deg",">20deg",">30deg",">40deg"),
hjust = -0.6,
vjust = 2,
ncol = 4,
nrow = 1)
test_stat_tibble <- all_rawdata %>%
filter(genotype != "CS female") %>%
#filter(genotype != "A") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
both_wing_index = 100*sum(min_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]>(15*pi/180),
na.rm = TRUE)/
length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]
))
aov1 <- aov(both_wing_index~genotype, data = test_stat_tibble)
TukeyHSD(aov1) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = both_wing_index ~ genotype, data = test_stat_tibble)
$genotype
diff lwr upr p adj
B-A 1.3374241 -4.537783 7.212632 0.9338504
C-A 1.9198669 -3.167234 7.006968 0.7588352
D-A 10.3410421 5.217313 15.464771 0.0000040
C-B 0.5824427 -5.190223 6.355108 0.9935904
D-B 9.0036180 3.198649 14.808587 0.0005495
D-C 8.4211753 3.415359 13.426992 0.0001503
wind <- 600
p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
mean_max_wing = mean(max_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
])
) %>%
ggplot(aes(x=genotype,y=mean_max_wing)) +
geom_boxplot() +
ylim(0,1.5)
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
mean_left_wing = -mean(wing_l_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
],na.rm = TRUE)
) %>%
ggplot(aes(x=genotype,y=mean_left_wing)) +
geom_boxplot() +
ylim(0,1.5)
p3 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
mean_right_wing = mean(wing_r_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
],na.rm = TRUE)
) %>%
ggplot(aes(x=genotype,y=mean_right_wing)) +
geom_boxplot() +
ylim(0,1.5)
p4 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
mean_min_wing = mean(min_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
])
) %>%
ggplot(aes(x=genotype,y=mean_min_wing)) +
geom_boxplot() +
ylim(0,1.5)
ggarrange(plotlist = list(p1,p4,p2,p3),
labels = c("max wing","min wings","left wing","right wing"),
hjust = -0.6,
vjust = 2,
ncol = 4,
nrow = 1)
all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
mean_min_wing = mean(min_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
])
) %>%
ggplot(aes(x=genotype,y=mean_min_wing)) +
geom_boxplot() +
ylim(0,.5)
all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
min_wing_gt_15 = sum(mean(min_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
])>15*pi/180)
) %>%
summarise(tot_min_wing_gt_15 = sum(min_wing_gt_15>0,na.rm = TRUE),
prop_min_wing_gt_15 = 100*sum(min_wing_gt_15>0,na.rm = TRUE)/length(min_wing_gt_15))There were 24 warnings (use warnings() to see them)
p1 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(min_wing_ang__rad > (15*pi/180)) %>%
filter(genotype == "A") %>%
ggplot(aes(x=-wing_l_ang__rad, y=wing_l_len__px)) +
geom_bin2d(bins = c(200,50)) +
xlim(0,2*pi) +
ylim(0,40) +
scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
theme_void() +
coord_polar()
p2 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(min_wing_ang__rad > (15*pi/180)) %>%
filter(genotype == "B") %>%
ggplot(aes(x=-wing_l_ang__rad, y=wing_l_len__px)) +
geom_bin2d(bins = c(200,50)) +
xlim(0,2*pi) +
ylim(0,40) +
scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
theme_void() +
coord_polar()
p3 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(min_wing_ang__rad > (15*pi/180)) %>%
filter(genotype == "C") %>%
ggplot(aes(x=-wing_l_ang__rad, y=wing_l_len__px)) +
geom_bin2d(bins = c(200,50)) +
xlim(0,2*pi) +
ylim(0,40) +
scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
theme_void() +
coord_polar()
p4 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(min_wing_ang__rad > (15*pi/180)) %>%
filter(genotype == "D") %>%
ggplot(aes(x=-wing_l_ang__rad, y=wing_l_len__px)) +
geom_bin2d(bins = c(200,50)) +
xlim(0,2*pi) +
ylim(0,40) +
scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
theme_void() +
coord_polar()
ggarrange(plotlist = list(p1,p2,p3,p4),
labels = c("A","B","C","D"),
#hjust = 1,
ncol = 2,
nrow = 2)
p1 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(min_wing_ang__rad > (15*pi/180)) %>%
filter(genotype == "A") %>%
ggplot(aes(x=wing_r_ang__rad, y=wing_r_len__px)) +
geom_bin2d(bins = c(200,50)) +
xlim(0,2*pi) +
ylim(0,40) +
scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
theme_void() +
coord_polar()
p2 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(min_wing_ang__rad > (15*pi/180)) %>%
filter(genotype == "B") %>%
ggplot(aes(x=wing_r_ang__rad, y=wing_r_len__px)) +
geom_bin2d(bins = c(200,50)) +
xlim(0,2*pi) +
ylim(0,40) +
scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
theme_void() +
coord_polar()
p3 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(min_wing_ang__rad > (15*pi/180)) %>%
filter(genotype == "C") %>%
ggplot(aes(x=wing_r_ang__rad, y=wing_r_len__px)) +
geom_bin2d(bins = c(200,50)) +
xlim(0,2*pi) +
ylim(0,40) +
scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
theme_void() +
coord_polar()
p4 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(min_wing_ang__rad > (15*pi/180)) %>%
filter(genotype == "D") %>%
ggplot(aes(x=wing_r_ang__rad, y=wing_r_len__px)) +
geom_bin2d(bins = c(200,50)) +
xlim(0,2*pi) +
ylim(0,40) +
scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
theme_void() +
coord_polar()
ggarrange(plotlist = list(p1,p2,p3,p4),
labels = c("A","B","C","D"),
#hjust = 1,
ncol = 2,
nrow = 2)
p1 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(min_wing_ang__rad > (15*pi/180)) %>%
filter(genotype == "A") %>%
ggplot() +
geom_bin2d(aes(x=wing_r_ang__rad, y=wing_r_len__px), bins = c(200,50)) +
geom_bin2d(aes(x=wing_l_ang__rad, y=wing_l_len__px), bins = c(200,50)) +
xlim(-pi,pi) +
ylim(0,60) +
scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
theme_void() +
coord_polar()
p2 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(min_wing_ang__rad > (15*pi/180)) %>%
filter(genotype == "B") %>%
ggplot() +
geom_bin2d(aes(x=wing_r_ang__rad, y=wing_r_len__px), bins = c(200,50)) +
geom_bin2d(aes(x=wing_l_ang__rad, y=wing_l_len__px), bins = c(200,50)) +
xlim(-pi,pi) +
ylim(0,60) +
scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
theme_void() +
coord_polar()
p3 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(min_wing_ang__rad > (15*pi/180)) %>%
filter(genotype == "C") %>%
ggplot() +
geom_bin2d(aes(x=wing_r_ang__rad, y=wing_r_len__px), bins = c(200,50)) +
geom_bin2d(aes(x=wing_l_ang__rad, y=wing_l_len__px), bins = c(200,50)) +
xlim(-pi,pi) +
ylim(0,60) +
scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
theme_void() +
coord_polar()
p4 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(min_wing_ang__rad > (15*pi/180)) %>%
filter(genotype == "D") %>%
ggplot() +
geom_bin2d(aes(x=wing_r_ang__rad, y=wing_r_len__px), bins = c(200,50)) +
geom_bin2d(aes(x=wing_l_ang__rad, y=wing_l_len__px), bins = c(200,50)) +
xlim(-pi,pi) +
ylim(0,60) +
scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
theme_void() +
coord_polar()
ggarrange(plotlist = list(p1,p2,p3,p4),
labels = c("A","B","C","D"),
#hjust = 1,
ncol = 2,
nrow = 2)
p1 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad < (2*pi/3)) %>%
filter(min_wing_ang__rad > (0)) %>%
filter(wing_l_ang__rad > (-2*pi/3)) %>%
filter(wing_r_ang__rad < (2*pi/3)) %>%
filter(wing_l_ang__rad < (0)) %>%
filter(wing_r_ang__rad > (0)) %>%
filter(dist_to_wall__mm > 2) %>%
filter(min_wing_ang__rad > (15*pi/180)) %>%
filter(genotype == "A") %>%
ggplot() +
geom_bin2d(aes(x=wing_r_ang__rad, y=wing_r_len__px), bins = c(200,50)) +
geom_bin2d(aes(x=wing_l_ang__rad, y=wing_l_len__px), bins = c(200,50)) +
xlim(-pi,pi) +
ylim(0,60) +
scale_fill_continuous(type = "viridis",limits = c(10,500),na.value = "white") +
theme_void() +
coord_polar()
p2 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad < (2*pi/3)) %>%
filter(min_wing_ang__rad > (0)) %>%
filter(wing_l_ang__rad > (-2*pi/3)) %>%
filter(wing_r_ang__rad < (2*pi/3)) %>%
filter(wing_l_ang__rad < (0)) %>%
filter(wing_r_ang__rad > (0)) %>%
filter(dist_to_wall__mm > 2) %>%
filter(min_wing_ang__rad > (15*pi/180)) %>%
filter(genotype == "B") %>%
ggplot() +
geom_bin2d(aes(x=wing_r_ang__rad, y=wing_r_len__px), bins = c(200,50)) +
geom_bin2d(aes(x=wing_l_ang__rad, y=wing_l_len__px), bins = c(200,50)) +
xlim(-pi,pi) +
ylim(0,60) +
scale_fill_continuous(type = "viridis",limits = c(10,500),na.value = "white") +
theme_void() +
coord_polar()
p3 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad < (2*pi/3)) %>%
filter(min_wing_ang__rad > (0)) %>%
filter(wing_l_ang__rad > (-2*pi/3)) %>%
filter(wing_r_ang__rad < (2*pi/3)) %>%
filter(wing_l_ang__rad < (0)) %>%
filter(wing_r_ang__rad > (0)) %>%
filter(dist_to_wall__mm > 2) %>%
filter(min_wing_ang__rad > (15*pi/180)) %>%
filter(genotype == "C") %>%
ggplot() +
geom_bin2d(aes(x=wing_r_ang__rad, y=wing_r_len__px), bins = c(200,50)) +
geom_bin2d(aes(x=wing_l_ang__rad, y=wing_l_len__px), bins = c(200,50)) +
xlim(-pi,pi) +
ylim(0,60) +
scale_fill_continuous(type = "viridis",limits = c(10,500),na.value = "white") +
theme_void() +
coord_polar()
p4 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad < (2*pi/3)) %>%
filter(min_wing_ang__rad > (0)) %>%
filter(wing_l_ang__rad > (-2*pi/3)) %>%
filter(wing_r_ang__rad < (2*pi/3)) %>%
filter(wing_l_ang__rad < (0)) %>%
filter(wing_r_ang__rad > (0)) %>%
filter(dist_to_wall__mm > 2) %>%
filter(min_wing_ang__rad > (15*pi/180)) %>%
filter(genotype == "D") %>%
ggplot() +
geom_bin2d(aes(x=wing_r_ang__rad, y=wing_r_len__px), bins = c(200,50)) +
geom_bin2d(aes(x=wing_l_ang__rad, y=wing_l_len__px), bins = c(200,50)) +
xlim(-pi,pi) +
ylim(0,60) +
scale_fill_continuous(type = "viridis",limits = c(10,500),na.value = "white") +
theme_void() +
coord_polar()
ggarrange(plotlist = list(p1,p2,p3,p4),
labels = c("A","B","C","D"),
#hjust = 1,
ncol = 2,
nrow = 2)
p1 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(min_wing_ang__rad > (15*pi/180)) %>%
filter(genotype == "A") %>%
ggplot() +
stat_density_2d(aes(x=wing_r_ang__rad, y=wing_r_len__px,fill = ..level..), geom = "polygon") +
stat_density_2d(aes(x=wing_l_ang__rad, y=wing_l_len__px,fill = ..level..), geom = "polygon") +
xlim(-pi,pi) +
ylim(0,60) +
#scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
theme_void() +
coord_polar()
p2 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(min_wing_ang__rad > (15*pi/180)) %>%
filter(genotype == "B") %>%
ggplot() +
stat_density_2d(aes(x=wing_r_ang__rad, y=wing_r_len__px,fill = ..level..), geom = "polygon") +
stat_density_2d(aes(x=wing_l_ang__rad, y=wing_l_len__px,fill = ..level..), geom = "polygon") +
xlim(-pi,pi) +
ylim(0,60) +
#scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
theme_void() +
coord_polar()
p3 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(min_wing_ang__rad > (15*pi/180)) %>%
filter(genotype == "C") %>%
ggplot() +
stat_density_2d(aes(x=wing_r_ang__rad, y=wing_r_len__px,fill = ..level..), geom = "polygon") +
stat_density_2d(aes(x=wing_l_ang__rad, y=wing_l_len__px,fill = ..level..), geom = "polygon") +
xlim(-pi,pi) +
ylim(0,60) +
#scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
theme_void() +
coord_polar()
p4 <- all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(min_wing_ang__rad > (15*pi/180)) %>%
filter(genotype == "D") %>%
ggplot() +
stat_density_2d(aes(x=wing_r_ang__rad, y=wing_r_len__px,fill = ..level..), geom = "polygon") +
stat_density_2d(aes(x=wing_l_ang__rad, y=wing_l_len__px,fill = ..level..), geom = "polygon") +
xlim(-pi,pi) +
ylim(0,60) +
#scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
theme_void() +
coord_polar()
ggarrange(plotlist = list(p1,p2,p3,p4),
labels = c("A","B","C","D"),
#hjust = 1,
ncol = 2,
nrow = 2)
all_rawdata %>%
filter(dist_to_other__mm > 2) %>%
filter(dist_to_other__mm < 10) %>%
filter(max_wing_ang__rad > (25*pi/180)) %>%
#filter(genotype == "D") %>%
ggplot(aes(x=genotype, y=wing_l_len__px))+
geom_boxplot()
Facing angle at start vs end of wing bout
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 36) %>%
slice(which.max(SmoothedCourtship):which.max(SmoothedCopulation)) %>%
ggplot(aes(x=Frame)) +
geom_point(aes(y=-wing_l_ang__rad, colour = "#F8766D")) +
geom_point(aes(y=wing_r_ang__rad, colour = "#00BFC4")) +
geom_point(aes(y=c(ifelse(max_wing_ang__rad>=(25*pi/180),1.6,-1)))) +
geom_point(aes(y=c(ifelse(WingGesture==1,WingGesture+0.8,WingGesture-1)))) +
#geom_point(aes(y=c(ifelse(WingGesture==1,WingGesture+0.6,WingGesture-1)))) +
ylim(0,2)
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_6") %>%
filter(Id == 7) %>%
slice(which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*600)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*600)),max(Frame))
)) %>%
ggplot(aes(x=Frame)) +
geom_point(aes(y=-wing_l_ang__rad, colour = "#F8766D")) +
geom_point(aes(y=wing_r_ang__rad, colour = "#00BFC4")) +
geom_point(aes(y=c(ifelse(max_wing_ang__rad>=(35*pi/180),1.6,-1)))) +
geom_point(aes(y=c(ifelse(WingGesture==1,WingGesture+0.8,WingGesture-1)))) +
#geom_point(aes(y=c(ifelse(WingGesture==1,WingGesture+0.6,WingGesture-1)))) +
ylim(0,2)
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 5) %>%
slice(which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*600)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*600)),max(Frame))
)) %>%
ggplot(aes(x=Frame)) +
geom_point(aes(y=-wing_l_ang__rad, colour = "#F8766D")) +
geom_point(aes(y=wing_r_ang__rad, colour = "#00BFC4")) +
geom_point(aes(y=c(ifelse(max_wing_ang__rad>=(35*pi/180),1.6,-1)))) +
geom_point(aes(y=c(ifelse(WingGesture==1,WingGesture+0.8,WingGesture-1)))) +
#geom_point(aes(y=c(ifelse(WingGesture==1,WingGesture+0.6,WingGesture-1)))) +
ylim(0,2)
Facing angle at start and end of each bout
temp <- all_rawdata %>%
filter(genotype!="CS female") %>%
filter(dist_to_other__mm > 2)
uniq_fly <- unique(temp$unique_fly)
facing_start_and_end_wing <- tibble()
for (fly in uniq_fly) {
temp2 <- filter(temp, unique_fly == fly)
bouts <- rle(temp2$WingGesture)
starts <- as.numeric()
for (i in seq(1,length(which(bouts$values==1)),1)) {
starts[i] = sum(bouts$lengths[1:(i*2)-1])+1
}
ends <- as.numeric()
for (i in seq(1,length(which(bouts$values==1)),1)) {
ends[i] = sum(bouts$lengths[1:(i*2)])
}
genotype = rep(unique(temp2$genotype),length(which(bouts$values==1)))
uni_id = rep(unique(temp2$unique_fly),length(which(bouts$values==1)))
temp_tibble <- tibble(unique_fly = uni_id,
genotype = genotype,
facing_at_start = temp2$facing_angle__rad[starts],
facing_at_end = temp2$facing_angle__rad[ends]
)
facing_start_and_end_wing <- bind_rows(facing_start_and_end_wing,temp_tibble)
}
facing_start_and_end_wing
p1 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_start)) +
geom_boxplot()
p2 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_end)) +
geom_boxplot()
p3 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_start/facing_at_end)) +
geom_boxplot() +
ylim(0,2)
ggarrange(plotlist = list(p1,p2,p3),
labels = c("Facing Angle at Start","Facing Angle at End","Ratio start:end"),
hjust = -0.2,
vjust = 1.6,
ncol = 3,
nrow = 1)
p1 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_start-facing_at_end)) +
geom_boxplot()+
ylim(-0.5,0.5)
p2 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_end-facing_at_start)) +
geom_boxplot()+
ylim(-0.5,0.5)
p3 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_start/facing_at_end)) +
geom_boxplot() +
ylim(0,2)
ggarrange(plotlist = list(p1,p2,p3),
labels = c("Start-End","End-Start","Ratio start:end"),
hjust = -0.2,
vjust = 1.6,
ncol = 3,
nrow = 1)
p1 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_start)) +
geom_violin()
p2 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_end)) +
geom_violin()
ggarrange(plotlist = list(p1,p2),
labels = c("Facing Angle at Start","Facing Angle at End"),
#hjust = -0.6,
#vjust = 2,
ncol = 2,
nrow = 1)
Mean facing angle at start and end of bout
temp <- all_rawdata %>%
filter(genotype!="CS female") %>%
filter(dist_to_other__mm > 2)
uniq_fly <- unique(temp$unique_fly)
facing_start_and_end_wing <- tibble()
for (fly in uniq_fly) {
temp2 <- filter(temp, unique_fly == fly)
bouts <- rle(temp2$WingGesture)
starts <- as.numeric()
for (i in seq(1,length(which(bouts$values==1)),1)) {
starts[i] = sum(bouts$lengths[1:(i*2)-1])+1
}
ends <- as.numeric()
for (i in seq(1,length(which(bouts$values==1)),1)) {
ends[i] = sum(bouts$lengths[1:(i*2)])
}
genotype = unique(temp2$genotype)
uni_id = unique(temp2$unique_fly)
temp_tibble <- tibble(unique_fly = uni_id,
genotype = genotype,
facing_at_start = mean(temp2$facing_angle__rad[starts]),
facing_at_end = mean(temp2$facing_angle__rad[ends])
)
facing_start_and_end_wing <- bind_rows(facing_start_and_end_wing,temp_tibble)
}
facing_start_and_end_wing
p1 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_start)) +
geom_boxplot() +
ylim(0,1)
p2 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_end)) +
geom_boxplot() +
ylim(0,1)
ggarrange(plotlist = list(p1,p2),
labels = c("mean Facing Angle at Start","mean Facing Angle at End"),
#hjust = -0.6,
#vjust = 2,
ncol = 2,
nrow = 1)
Median facing angle at start and end of bout (using WingGesture)
temp <- all_rawdata %>%
filter(genotype!="CS female") %>%
filter(dist_to_other__mm > 2)
uniq_fly <- unique(temp$unique_fly)
facing_start_and_end_wing <- tibble()
for (fly in uniq_fly) {
temp2 <- filter(temp, unique_fly == fly)
bouts <- rle(temp2$WingGesture)
starts <- as.numeric()
for (i in seq(1,length(which(bouts$values==1)),1)) {
starts[i] = sum(bouts$lengths[1:(i*2)-1])+1
}
ends <- as.numeric()
for (i in seq(1,length(which(bouts$values==1)),1)) {
ends[i] = sum(bouts$lengths[1:(i*2)])
}
genotype = unique(temp2$genotype)
uni_id = unique(temp2$unique_fly)
temp_tibble <- tibble(unique_fly = uni_id,
genotype = genotype,
facing_at_start = median(temp2$facing_angle__rad[starts]),
facing_at_end = median(temp2$facing_angle__rad[ends])
)
facing_start_and_end_wing <- bind_rows(facing_start_and_end_wing,temp_tibble)
}
facing_start_and_end_wing
p1 <- all_rawdata %>%
filter(genotype!="CS female") %>%
group_by(unique_fly) %>%
filter(dist_to_other__mm > 2) %>%
filter(WingGesture==1) %>%
summarise(genotype = unique(genotype),
median_facing = median(facing_angle__rad)) %>%
ggplot(aes(x=genotype,y=median_facing)) +
geom_boxplot() +
ylim(0,0.5)
p2 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_start)) +
geom_boxplot() +
ylim(0,0.5)
p3 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_end)) +
geom_boxplot() +
ylim(0,0.5)
p4 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_start/facing_at_end)) +
geom_boxplot() #+
#ylim(0,0.5)
ggarrange(plotlist = list(p1,p2,p3,p4),
labels = c("median Facing angle throughout","median at Start of each bout","median at End of each bout","ratio start:end"),
hjust = -0.2,
vjust = 2,
ncol = 4,
nrow = 1)
Median facing angle at start and end of bout (using >35deg)
temp <- all_rawdata %>%
filter(genotype!="CS female") %>%
filter(dist_to_other__mm > 2)
uniq_fly <- unique(temp$unique_fly)
facing_start_and_end_wing <- tibble()
for (fly in uniq_fly) {
temp2 <- filter(temp, unique_fly == fly)
#bouts <- rle(temp2$WingGesture)
temp2$bin_max_wing <- ifelse(temp2$max_wing_ang__rad >= (35*pi/180),1,0)
temp2$bin_max_wing <- temp2$bin_max_wing %>% replace_na(0)
bouts <- rle(temp2$bin_max_wing)
starts <- as.numeric()
for (i in seq(1,length(which(bouts$values==1)),1)) {
starts[i] = sum(bouts$lengths[1:(i*2)-1])+1
}
ends <- as.numeric()
for (i in seq(1,length(which(bouts$values==1)),1)) {
ends[i] = sum(bouts$lengths[1:(i*2)])
}
genotype = unique(temp2$genotype)
uni_id = unique(temp2$unique_fly)
temp_tibble <- tibble(unique_fly = uni_id,
genotype = genotype,
facing_at_start = median(temp2$facing_angle__rad[starts]),
facing_at_end = median(temp2$facing_angle__rad[ends])
)
facing_start_and_end_wing <- bind_rows(facing_start_and_end_wing,temp_tibble)
}
facing_start_and_end_wing
p1 <- all_rawdata %>%
filter(genotype!="CS female") %>%
group_by(unique_fly) %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
summarise(genotype = unique(genotype),
median_facing = median(facing_angle__rad)) %>%
ggplot(aes(x=genotype,y=median_facing)) +
geom_boxplot() +
ylim(0,0.5)
p2 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_start)) +
geom_boxplot() +
ylim(0,0.5)
p3 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_end)) +
geom_boxplot() +
ylim(0,0.5)
p4 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_start/facing_at_end)) +
geom_boxplot() #+
#ylim(0,0.5)
ggarrange(plotlist = list(p1,p2,p3,p4),
labels = c("median Facing angle throughout","median at Start of each bout","median at End of each bout","ratio start:end"),
hjust = -0.2,
vjust = 2,
ncol = 4,
nrow = 1)
Facing angles with wing extended using WingGesture
p1 <- all_rawdata %>%
filter(genotype!="CS female") %>%
group_by(unique_fly) %>%
filter(dist_to_other__mm > 2) %>%
filter(WingGesture==1) %>%
ggplot(aes(x=genotype,y=facing_angle__rad)) +
geom_boxplot() +
ylim(0,1)
p2 <- all_rawdata %>%
filter(genotype!="CS female") %>%
group_by(unique_fly) %>%
filter(dist_to_other__mm > 2) %>%
filter(WingGesture==1) %>%
summarise(genotype = unique(genotype),
median_facing = median(facing_angle__rad)) %>%
ggplot(aes(x=genotype,y=median_facing)) +
geom_boxplot() +
ylim(0,1)
p3 <- all_rawdata %>%
filter(genotype!="CS female") %>%
group_by(unique_fly) %>%
filter(dist_to_other__mm > 2) %>%
filter(WingGesture==1) %>%
summarise(genotype = unique(genotype),
mean_facing = mean(facing_angle__rad)) %>%
ggplot(aes(x=genotype,y=mean_facing)) +
geom_boxplot() +
ylim(0,1)
ggarrange(plotlist = list(p1,p2,p3),
labels = c("Facing Angle","median Facing Angle","mean Facing Angle"),
#hjust = -0.6,
#vjust = 2,
ncol = 3,
nrow = 1)
Facing angles with wing extended using max_wing>35
p1 <- all_rawdata %>%
filter(genotype!="CS female") %>%
group_by(unique_fly) %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
ggplot(aes(x=genotype,y=facing_angle__rad)) +
geom_boxplot() +
ylim(0,1)
p2 <- all_rawdata %>%
filter(genotype!="CS female") %>%
group_by(unique_fly) %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
summarise(genotype = unique(genotype),
median_facing = median(facing_angle__rad)) %>%
ggplot(aes(x=genotype,y=median_facing)) +
geom_boxplot() +
ylim(0,1)
p3 <- all_rawdata %>%
filter(genotype!="CS female") %>%
group_by(unique_fly) %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
summarise(genotype = unique(genotype),
mean_facing = mean(facing_angle__rad)) %>%
ggplot(aes(x=genotype,y=mean_facing)) +
geom_boxplot() +
ylim(0,1)
ggarrange(plotlist = list(p1,p2,p3),
labels = c("Facing Angle","median Facing Angle","mean Facing Angle"),
#hjust = -0.6,
#vjust = 2,
ncol = 3,
nrow = 1)
test_stat_tibble0 <- all_rawdata %>%
filter(genotype!="CS female") %>%
group_by(unique_fly) %>%
filter(dist_to_other__mm > 2) %>%
filter(max_wing_ang__rad > (35*pi/180)) %>%
summarise(genotype = unique(genotype),
facing = median(facing_angle__rad))
aov0 <- aov(facing~genotype,data = test_stat_tibble0)
#summary(aov0)
TukeyHSD(aov0) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = facing ~ genotype, data = test_stat_tibble0)
$genotype
diff lwr upr p adj
B-A -0.064278836 -0.2941883 0.1656306 0.8852971
C-A -0.017473865 -0.2165430 0.1815953 0.9957523
D-A -0.012774271 -0.2132767 0.1877282 0.9983603
C-B 0.046804972 -0.1790918 0.2727017 0.9489797
D-B 0.051504566 -0.1756563 0.2786654 0.9345696
D-C 0.004699594 -0.1911887 0.2005879 0.9999116
test <- all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 36) temp2 <- test
#bouts <- rle(temp2$WingGesture)
temp2$bin_max_wing <- ifelse(temp2$max_wing_ang__rad > (35*pi/180),1,0)
temp2$bin_max_wing <- temp2$bin_max_wing %>% replace_na(0)
bouts <- rle(temp2$bin_max_wing)
starts <- as.numeric()
for (i in seq(1,length(which(bouts$values==1)),1)) {
starts[i] = sum(bouts$lengths[1:(i*2)-1])+1
}
ends <- as.numeric()
for (i in seq(1,length(which(bouts$values==1)),1)) {
ends[i] = sum(bouts$lengths[1:(i*2)])
}
genotype = rep(unique(temp2$genotype),length(which(bouts$values==1)))
uni_id = rep(unique(temp2$unique_fly),length(which(bouts$values==1)))
temp_tibble <- tibble(unique_fly = uni_id,
genotype = genotype,
facing_at_start = temp2$facing_angle__rad[starts],
facing_at_end = temp2$facing_angle__rad[ends]
)
temp_tibblestarts = for (i in seq(1,length(which(bouts$values==1)),1)) {
starts[i] = sum(bouts$lengths[1:(i*2)-1])+1
}
startsNULLtest$bin_max_wing <- ifelse(test$max_wing_ang__rad > (35*pi/180),1,0)
test$bin_max_wing <- test$bin_max_wing %>% replace_na(0)
#test$bin_max_wing
bouts <- rle(test$bin_max_wing)
boutsRun Length Encoding
lengths: int [1:393] 675 3 2 14 2 22 212 15 15 2 ...
values : num [1:393] 0 1 0 1 0 1 0 1 0 1 ...bouts <- rle(test$WingGesture)
boutsRun Length Encoding
lengths: int [1:1361] 666 5 3 45 198 1 2 5 4 17 ...
values : num [1:1361] 0 1 0 1 0 1 0 1 0 1 ...test <- all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 36)
test$bin_max_wing <- ifelse(test$max_wing_ang__rad > (35*pi/180),1,0)
test$bin_max_wing <- test$bin_max_wing %>% replace_na(0)
test %>%
slice(which.max(SmoothedCourtship):which.max(SmoothedCopulation)) %>%
ggplot(aes(x=Frame)) +
geom_point(aes(y=-wing_l_ang__rad, colour = "#F8766D")) +
geom_point(aes(y=wing_r_ang__rad, colour = "#00BFC4")) +
geom_point(aes(y=c(ifelse(bin_max_wing==1,1.6,-1)))) +
geom_point(aes(y=c(ifelse(max_wing_ang__rad>=(35*pi/180),1.8,-1)))) +
geom_point(aes(y=c(ifelse(WingGesture==1,WingGesture+1,WingGesture-1)))) +
ylim(0,2.2)
sum(test$bin_max_wing[which.max(test$SmoothedCourtship):which.max(test$SmoothedCopulation)]==test$WingGesture[which.max(test$SmoothedCourtship):which.max(test$SmoothedCopulation)])[1] 1230sum(test$bin_max_wing[which.max(test$SmoothedCourtship):which.max(test$SmoothedCopulation)]!=test$WingGesture[which.max(test$SmoothedCourtship):which.max(test$SmoothedCopulation)])[1] 35test <- all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_123_3") %>%
filter(Id == 1)
test$bin_max_wing <- ifelse(test$max_wing_ang__rad > (35*pi/180),1,0)
test$bin_max_wing <- test$bin_max_wing %>% replace_na(0)
test %>%
#slice(which.max(SmoothedCourtship):which.max(SmoothedCopulation)) %>%
ggplot(aes(x=Frame)) +
geom_point(aes(y=-wing_l_ang__rad, colour = "#F8766D")) +
geom_point(aes(y=wing_r_ang__rad, colour = "#00BFC4")) +
geom_point(aes(y=c(ifelse(bin_max_wing==1,1.6,-1)))) +
geom_point(aes(y=c(ifelse(max_wing_ang__rad>=(35*pi/180),1.8,-1)))) +
geom_point(aes(y=c(ifelse(WingGesture==1,WingGesture+1,WingGesture-1)))) +
ylim(0,2.2)
gg_color_hue(4)[1] "#F8766D" "#7CAE00" "#00BFC4" "#C77CFF"test <- all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_123_3") %>%
filter(Id == 1) %>%
filter(dist_to_other__mm > 2)
test$bin_max_wing <- ifelse(test$max_wing_ang__rad >= (35*pi/180),1,0)
test$bin_max_wing <- test$bin_max_wing %>% replace_na(0)
test %>%
ggplot(aes(x=Frame)) +
geom_point(aes(y=dist_to_other__mm, colour = "#F8766D")) +
geom_point(aes(y=SmoothedDistToOther-1, colour = "#7CAE00")) +
geom_point(aes(y=(SmoothedCopulation-2), colour= "#00BFC4")) +
geom_point(aes(y=c(ifelse(bin_max_wing==1,-3,-10)))) +
geom_point(aes(y=c(ifelse(max_wing_ang__rad>=(35*pi/180),-4,-10)))) +
geom_point(aes(y=c(ifelse(WingGesture==1,-6,-10)))) +
geom_point(aes(y=c(ifelse(SmoothedCourtship==1,20,-10)))) +
ylim(-6,20)
max(test$Frame)[1] 5974temp2 <- all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_123_3") %>%
filter(Id == 1) %>%
filter(dist_to_other__mm > 2)
#bouts <- rle(temp2$WingGesture)
temp2$bin_max_wing <- ifelse(temp2$max_wing_ang__rad >= (35*pi/180),1,0)
temp2$bin_max_wing <- temp2$bin_max_wing %>% replace_na(0)
bouts <- rle(test$bin_max_wing)
starts <- as.numeric()
for (i in seq(1,length(which(bouts$values==1)),1)) {
starts[i] = sum(bouts$lengths[1:(i*2)-1])+1
}
ends <- as.numeric()
for (i in seq(1,length(which(bouts$values==1)),1)) {
ends[i] = sum(bouts$lengths[1:(i*2)])
}
genotype = unique(temp2$genotype)
uni_id = unique(temp2$unique_fly)
temp_tibble <- tibble(unique_fly = uni_id,
genotype = genotype,
facing_at_start = median(temp2$facing_angle__rad[starts]),
facing_at_end = median(temp2$facing_angle__rad[ends])
)
temp_tibblebouts <- rle(test$bin_max_wing)
boutsRun Length Encoding
lengths: int [1:198] 631 3 57 5 363 34 357 1 23 1 ...
values : num [1:198] 0 1 0 1 0 1 0 1 0 1 ...all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_123_3") %>%
filter(Id == 21) %>%
ggplot(aes(x=Frame)) +
geom_point(aes(y=SmoothedCourtship, colour = "#F8766D")) +
geom_point(aes(y=(SmoothedCopulation+0.1), colour= "#00BFC4")) #+
#xlim(2000,3000)all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 36) %>%
#slice(which.max(SmoothedCourtship):which.max(SmoothedCopulation)) %>%
ggplot(aes(x=Frame)) +
geom_point(aes(y=wing_l_ang__rad, colour = "#F8766D")) +
geom_point(aes(y=wing_r_ang__rad, colour = "#00BFC4"))
NAall_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 36) %>%
slice(which.max(SmoothedCourtship):which.max(SmoothedCopulation)) %>%
ggplot(aes(x=Frame)) +
geom_point(aes(y=-wing_l_ang__rad, colour = "#F8766D")) +
geom_point(aes(y=wing_r_ang__rad, colour = "#00BFC4")) +
geom_point(aes(y=c(ifelse(WingGesture==1,WingGesture+0.6,WingGesture-0.2))))
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 36) %>%
slice(which.max(SmoothedCourtship):which.max(SmoothedCopulation)) %>%
ggplot(aes(x=Frame)) +
geom_point(aes(y=-wing_l_ang__rad, colour = "#F8766D")) +
geom_point(aes(y=wing_r_ang__rad, colour = "#00BFC4")) +
geom_point(aes(y=c(ifelse(WingGesture==1,WingGesture+0.6,NA))))
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 36) %>%
ggplot(aes(x=Frame,y=dist_to_other__mm)) +
geom_point(aes(colour=c(ifelse(SmoothedDistToOther>0.5,"not copulating","copulating"))))+
geom_point(aes(y=c(ifelse(SmoothedCopulation==1,SmoothedCopulation+6,NA))))
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 36) %>%
ggplot(aes(x=Frame)) +
geom_point(aes(y=SmoothedDistToOther, colour = "#F8766D")) +
geom_point(aes(y=(SmoothedCopulation+0.1), colour= "#00BFC4")) +
xlim(2000,3000)
all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 36) %>%
ggplot(aes(x=Frame)) +
geom_point(aes(y=SmoothedCourtship, colour = "#F8766D")) +
geom_point(aes(y=(SmoothedCopulation+0.1), colour= "#00BFC4")) #+
#xlim(2000,3000)Trying to make functions for courtship window and calculate indices… they aren’t working right, and seem to be ignoring the grouping of the tibble and returning the same value for every individual…
If i have to for loop over the groups of a tibble, the below line might be usefull. dim(unique(all_rawdata[all_rawdata %>% group_by(genotype) %>% group_vars()]))[1]
courtship_window <- function(input,wind=600,...){
temp_tibble <- summarise(input, start_of_courtship = which.max(SmoothedCourtship),
end_of_courtship = ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
)
return(temp_tibble$start_of_courtship:temp_tibble$end_of_courtship)
#return(c(temp_tibble$start_of_courtship,temp_tibble$end_of_courtship))
#return(temp_tibble)
}courtship_window <- function(input,wind=600,...){
temp_tibble <- summarise(input, start_of_courtship = which.max(SmoothedCourtship),
end_of_courtship = ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
)
#return(temp_tibble$start_of_courtship:temp_tibble$end_of_courtship)
return(c(temp_tibble$start_of_courtship,temp_tibble$end_of_courtship))
#return(temp_tibble)
}courtship_window(test, wind = 600)temp <- all_rawdata %>%
filter(genotype!="CS female")
uniq_fly <- unique(temp$unique_fly)
courting_frames <- tibble()
for (fly in uniq_fly) {
temp2 <- temp %>% filter(unique_fly == fly)
temp2 <- slice(temp2, which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
))
courting_frames <- bind_rows(courting_frames,temp2)
}
courting_framestest <- all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_123_3") %>%
filter(Id == 1) %>%
filter(dist_to_other__mm > 2)
#test$bin_max_wing <- ifelse(test$max_wing_ang__rad >= (35*pi/180),1,0)
#test$bin_max_wing <- test$bin_max_wing %>% replace_na(0)
test %>%
ggplot(aes(x=Frame)) +
geom_point(aes(y=dist_to_other__mm, colour = "#F8766D")) +
geom_point(aes(y=SmoothedDistToOther-1, colour = "#7CAE00")) +
geom_point(aes(y=(SmoothedCopulation-2), colour= "#00BFC4")) +
#geom_point(aes(y=c(ifelse(bin_max_wing==1,-3,-10)))) +
geom_point(aes(y=c(ifelse(max_wing_ang__rad>=(35*pi/180),-4,-10)))) +
geom_point(aes(y=c(ifelse(WingGesture==1,-6,-10)))) +
geom_point(aes(y=c(ifelse(SmoothedCourtship==1,20,-10)))) +
ylim(-6,20)test <- courting_frames %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_123_3") %>%
filter(Id == 1) #%>%
#filter(dist_to_other__mm > 2)
#test$bin_max_wing <- ifelse(test$max_wing_ang__rad >= (35*pi/180),1,0)
#test$bin_max_wing <- test$bin_max_wing %>% replace_na(0)
test %>%
ggplot(aes(x=Frame)) +
geom_point(aes(y=dist_to_other__mm, colour = "#F8766D")) +
geom_point(aes(y=ifelse(SmoothedDistToOther==1,1,-10), colour = "#7CAE00")) +
geom_point(aes(y=ifelse(SmoothedCopulation==0,-2,-10), colour= "#00BFC4")) +
geom_point(aes(y=ifelse(Copulation==0,-1,-10), colour= "#00BFC4")) +
#geom_point(aes(y=c(ifelse(bin_max_wing==1,-3,-10)))) +
geom_point(aes(y=c(ifelse(max_wing_ang__rad>=(35*pi/180),-4,-10)))) +
geom_point(aes(y=c(ifelse(WingGesture==1,-6,-10)))) +
geom_point(aes(y=c(ifelse(SmoothedCourtship==1,20,-10)))) +
geom_point(aes(y=c(ifelse(Courtship==1,18,-10)))) +
ylim(-6,20)calculate_indices <- function(input = .,feature,jaaba=TRUE,thresh=NULL,wind=600){
if (feature %in% names(input)) {
var_ind = paste0(feature,"_index")
if (jaaba) {
temp_tibble <- summarise(input, genotype = unique(genotype),
!!var_ind := 100*sum(input[[feature]][which.max(input[["SmoothedCourtship"]]):ifelse(which.max(input[["SmoothedCopulation"]]) > which.max(input[["SmoothedCourtship"]]),
ifelse(which.max(input[["SmoothedCopulation"]]) <= (which.max(input[["SmoothedCourtship"]])+(25*wind)),
which.max(input[["SmoothedCopulation"]]),
min((which.max(input[["SmoothedCourtship"]])+(25*wind)),max(input[["Frame"]]))
),
min((which.max(input[["SmoothedCourtship"]])+(25*wind)),max(input[["Frame"]]))
)])/
length(input[["Frame"]][which.max(input[["SmoothedCourtship"]]):ifelse(which.max(input[["SmoothedCopulation"]]) > which.max(input[["SmoothedCourtship"]]),
ifelse(which.max(input[["SmoothedCopulation"]]) <= (which.max(input[["SmoothedCourtship"]])+(25*wind)),
which.max(input[["SmoothedCopulation"]]),
min((which.max(input[["SmoothedCourtship"]])+(25*wind)),max(input[["Frame"]]))
),
min((which.max(input[["SmoothedCourtship"]])+(25*wind)),max(input[["Frame"]]))
)])
)
} else {
temp_tibble <- summarise(input, genotype = unique(genotype),
!!var_ind := 100*sum(input[[feature]][which.max(input[["SmoothedCourtship"]]):ifelse(which.max(input[["SmoothedCopulation"]]) > which.max(input[["SmoothedCourtship"]]),
ifelse(which.max(input[["SmoothedCopulation"]]) <= (which.max(input[["SmoothedCourtship"]])+(25*wind)),
which.max(input[["SmoothedCopulation"]]),
min((which.max(input[["SmoothedCourtship"]])+(25*wind)),max(input[["Frame"]]))
),
min((which.max(input[["SmoothedCourtship"]])+(25*wind)),max(input[["Frame"]]))
)]>thresh,na.rm = TRUE)/
length(input[["Frame"]][which.max(input[["SmoothedCourtship"]]):ifelse(which.max(input[["SmoothedCopulation"]]) > which.max(input[["SmoothedCourtship"]]),
ifelse(which.max(input[["SmoothedCopulation"]]) <= (which.max(input[["SmoothedCourtship"]])+(25*wind)),
which.max(input[["SmoothedCopulation"]]),
min((which.max(input[["SmoothedCourtship"]])+(25*wind)),max(input[["Frame"]]))
),
min((which.max(input[["SmoothedCourtship"]])+(25*wind)),max(input[["Frame"]]))
)])
)
}
return(temp_tibble)
} else {
message(paste0(feature, " does not exist in table"))
}
} calculate_indices <- function(input,feature,jaaba=TRUE,thresh=NULL){
if (feature %in% names(input)) {
var_ind = paste0(feature,"_index")
if (jaaba) {
temp_tibble <- summarise(input, genotype = unique(genotype),
!!var_ind := 100*sum(input[[feature]][courtship_window(input)])/
length(input[["Frame"]][courtship_window(input)])
)
} else {
temp_tibble <- summarise(input, genotype = unique(genotype),
!!var_ind := 100*sum(input[[feature]][courtship_window(input)]>thresh,na.rm = TRUE)/
length(input[["Frame"]][courtship_window(input)])
)
}
return(temp_tibble)
} else {
message(paste0(feature, " does not exist in table"))
}
} p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(unique_fly) %>%
calculate_indices(feature = "max_wing_ang__rad",jaaba = FALSE,thresh = (25*pi/180)) %>%
ggplot(aes(x=genotype,y=max_wing_ang__rad_index)) +
geom_boxplot()
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(unique_fly) %>%
calculate_indices(feature = "wing_l_ang__rad",jaaba = FALSE,thresh = (25*pi/180)) %>%
ggplot(aes(x=genotype,y=wing_l_ang__rad_index)) +
geom_boxplot()
p3 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(unique_fly) %>%
calculate_indices(feature = "wing_r_ang__rad",jaaba = FALSE,thresh = (25*pi/180)) %>%
ggplot(aes(x=genotype,y=wing_r_ang__rad_index)) +
geom_boxplot()
ggarrange(plotlist = list(p1,p2,p3),
labels = c("wing index","left wing","right wing"),
hjust = -0.6,
vjust = 2,
ncol = 3,
nrow = 1)
p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
wing_index = 100*sum(max_wing_ang__rad[courtship_window(input)]>(40*pi/180),
na.rm = TRUE)/
length(Frame[courtship_window(input)]
)) %>%
ggplot(aes(x=genotype,y=wing_index)) +
geom_boxplot()
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
left_wing_index = 100*sum(wing_l_ang__rad[courtship_window(input)]<(-40*pi/180),
na.rm = TRUE)/
length(Frame[courtship_window(input)]
)) %>%
ggplot(aes(x=genotype,y=left_wing_index)) +
geom_boxplot()
p3 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
right_wing_index = 100*sum(wing_r_ang__rad[courtship_window(input)]>(40*pi/180),
na.rm = TRUE)/
length(Frame[courtship_window(input)]
)) %>%
ggplot(aes(x=genotype,y=right_wing_index)) +
geom_boxplot()
ggarrange(plotlist = list(p1,p2,p3),
labels = c("wing index","left wing","right wing"),
hjust = -0.6,
vjust = 2,
ncol = 3,
nrow = 1)
p1 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
wing_index = 100*sum(max_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]>(40*pi/180),
na.rm = TRUE)/
length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]
)) %>%
ggplot(aes(x=genotype,y=wing_index)) +
geom_boxplot()
p2 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
left_wing_index = 100*sum(wing_l_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]<(-40*pi/180),
na.rm = TRUE)/
length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]
)) %>%
ggplot(aes(x=genotype,y=left_wing_index)) +
geom_boxplot()
p3 <- all_rawdata %>%
filter(genotype != "CS female") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
summarise(genotype = unique(genotype),
right_wing_index = 100*sum(wing_r_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]>(40*pi/180),
na.rm = TRUE)/
length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)),
which.max(SmoothedCopulation),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
),
min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
)
]
)) %>%
ggplot(aes(x=genotype,y=right_wing_index)) +
geom_boxplot()
ggarrange(plotlist = list(p1,p2,p3),
labels = c("wing index","left wing","right wing"),
hjust = -0.6,
vjust = 2,
ncol = 3,
nrow = 1)all_rawdata %>%
filter(genotype != "CS female") %>%
filter(genotype == "D") %>%
group_by(genotype) %>%
group_by(unique_fly) %>%
calculate_indices(feature = "max_wing_ang__rad",jaaba = FALSE,thresh = (25*pi/180))test <- all_rawdata %>%
filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>%
filter(Id == 36) calculate_indices(input = test,feature = "WingGesture",jaaba = TRUE)calculate_indices(input = test,feature = "max_wing_ang__rad",jaaba = FALSE,thresh = (25*pi/180))calculate_indices(input = test,feature = "max_wing_angle__rad",jaaba = FALSE,thresh = (25*pi/180))---
title: "**Male courtship behaviour - aDN > TNT**"
author: "Aaron M. Allen"
date: "24 February 2020"
output: html_notebook
---

<br/>
<br/>

# **Setup**

```{r message=FALSE}
library("tidyverse")
library("ggpubr")
library("zoo")
setwd("/mnt/LocalData/behaviour/aDN/aDN_behaviour")
```


<br/>
<br/>


### Colours ...
 
```{r}
gg_color_hue <- function(n) {
  hues = seq(15, 375, length = n + 1)
  hcl(h = hues, l = 65, c = 100)[1:n]
}
```

```{r}
gg_color_hue(2)
```



<br/>
<br/>



```{r message=FALSE}
genotypes <- read_tsv("../2019_03_06_Courtship/genotype.tsv",col_names = TRUE)
genotypes
```



<br/>
<br/>






```{r message=FALSE}
# indices_list <- list.files("raw data/") %>% str_subset("_Indices.csv")
indices_list <- list.files("../2019_03_06_Courtship/",recursive = TRUE) %>% str_subset("_Indices.csv") %>% str_subset("Male")
all_indices <- tibble()
for (indices_file in indices_list) {
  video_name <- indices_file %>% str_remove("/.*")
  temp <- read_csv(paste0("../2019_03_06_Courtship/",indices_file))
  temp <- temp %>%
    mutate(video = video_name)
  all_indices <- bind_rows(all_indices,temp)
}
all_indices
```




```{r message=FALSE}
all_male_indices <- left_join(x = genotypes,y = all_indices,by=c("video"="video","fly_id"="FlyId"))
```




```{r}
all_male_indices %>% 
  group_by(genotype) %>% 
  summarise(n = length(genotype))
```

```{r}
unique(all_male_indices$genotype)
```



<br/>
<br/>



```{r message=FALSE}
ggplot(all_male_indices,aes(x=genotype,y=CourtshipIndexWithFacing)) + 
  geom_boxplot()
```


<br/>
<br/>



```{r message=FALSE, fig.width=12, fig.height=3}
p1 <- ggplot(all_male_indices,aes(x=genotype,y=CourtshipIndex)) + 
  geom_boxplot()
p2 <- ggplot(all_male_indices,aes(x=genotype,y=CourtshipIndexWithFacing)) + 
  geom_boxplot()
p3 <- ggplot(all_male_indices,aes(x=genotype,y=TotalCCI)) + 
  geom_boxplot()
p4 <- ggplot(all_male_indices,aes(x=genotype,y=TotalCCIwFacing)) + 
  geom_boxplot()
ggarrange(plotlist = list(p1,p2,p3,p4),
          # labels = c("ApproachingIndex",
          #            "ContactIndex",
          #            "EncirclingIndex",
          #            "FacingIndex",
          #            "TurningIndex",
          #            "WingIndex"),
          # hjust = 1,
          ncol = 4,
          nrow = 1)
```


<br/>
<br/>




```{r message=FALSE, fig.width=12, fig.height=8}
p1 <- ggplot(all_male_indices,aes(x=genotype,y=ApproachingIndex)) + 
  geom_boxplot()
p2 <- ggplot(all_male_indices,aes(x=genotype,y=ContactIndex)) + 
  geom_boxplot()
p3 <- ggplot(all_male_indices,aes(x=genotype,y=EncirclingIndex)) + 
  geom_boxplot()
p4 <- ggplot(all_male_indices,aes(x=genotype,y=FacingIndex)) + 
  geom_boxplot()
p5 <- ggplot(all_male_indices,aes(x=genotype,y=TurningIndex)) + 
  geom_boxplot()
p6 <- ggplot(all_male_indices,aes(x=genotype,y=WingIndex)) + 
  geom_boxplot()
ggarrange(plotlist = list(p1,p2,p3,p4,p5,p6),
          # labels = c("ApproachingIndex",
          #            "ContactIndex",
          #            "EncirclingIndex",
          #            "FacingIndex",
          #            "TurningIndex",
          #            "WingIndex"),
          # hjust = 1,
          ncol = 3,
          nrow = 2)
```



<br/>
<br/>





```{r message=FALSE, fig.width=10, fig.height=4}
p1 <- ggplot(all_male_indices,aes(x=genotype,y=CourtshipInitiation)) + 
  geom_boxplot()
p2 <- ggplot(all_male_indices,aes(x=genotype,y=CourtshipTermination)) + 
  geom_boxplot()
p3 <- ggplot(all_male_indices,aes(x=genotype,y=CourtshipDuration)) + 
  geom_boxplot()
ggarrange(plotlist = list(p1,p2,p3),
          ncol = 3,
          nrow = 1)
```


<br/>
<br/>



```{r message=FALSE}
df <- all_male_indices %>%
  select(genotype,CourtshipTermination) %>% 
  group_by(genotype) %>% 
  mutate(len=length(CourtshipTermination))
#ggplot(df,aes(x=CourtshipTermination,color=genotype)) + geom_step(aes(len=len,y=..y.. * len),stat="ecdf") 
ggplot(df,aes(x=CourtshipTermination,color=genotype)) + geom_step(aes(y=..y..),stat="ecdf")
```


<br/>
<br/>



```{r message=FALSE}
df <- all_male_indices %>%
  select(genotype,CourtshipDuration) %>% 
  group_by(genotype) %>% 
  mutate(len=length(CourtshipDuration))
#ggplot(df,aes(x=CourtshipDuration,color=genotype)) + geom_step(aes(len=len,y=..y.. * len),stat="ecdf") 
ggplot(df,aes(x=CourtshipDuration,color=genotype)) + geom_step(aes(y=..y..),stat="ecdf")
```




<br/>
<br/>




```{r message=FALSE, warning=FALSE, fig.width=16, fig.height=8}
p1 <- ggplot(all_male_indices,aes(x=genotype,y=ApproachingBoutLength)) + 
  geom_boxplot()
p2 <- ggplot(all_male_indices,aes(x=genotype,y=ContactBoutLength)) + 
  geom_boxplot()
p3 <- ggplot(all_male_indices,aes(x=genotype,y=EncirclingBoutLength)) + 
  geom_boxplot()
p4 <- ggplot(all_male_indices,aes(x=genotype,y=FacingBoutLength)) + 
  geom_boxplot()
p5 <- ggplot(all_male_indices,aes(x=genotype,y=TurningBoutLength)) + 
  geom_boxplot()
p6 <- ggplot(all_male_indices,aes(x=genotype,y=WingBoutLength)) + 
  geom_boxplot()
p7 <- ggplot(all_male_indices,aes(x=genotype,y=ApproachingBoutInterval)) + 
  geom_boxplot()
p8 <- ggplot(all_male_indices,aes(x=genotype,y=ContactBoutInterval)) + 
  geom_boxplot()
p9 <- ggplot(all_male_indices,aes(x=genotype,y=EncirclingBoutInterval)) + 
  geom_boxplot()
p10 <- ggplot(all_male_indices,aes(x=genotype,y=FacingBoutInterval)) + 
  geom_boxplot()
p11 <- ggplot(all_male_indices,aes(x=genotype,y=TurningBoutInterval)) + 
  geom_boxplot()
p12 <- ggplot(all_male_indices,aes(x=genotype,y=WingBoutInterval)) + 
  geom_boxplot()
ggarrange(plotlist = list(p1,p2,p3,p4,p5,p6,
                          p7,p8,p9,p10,p11,p12),
          # labels = c("ApproachingIndex",
          #            "ContactIndex",
          #            "EncirclingIndex",
          #            "FacingIndex",
          #            "TurningIndex",
          #            "WingIndex"),
          # hjust = 1,
          ncol = 6,
          nrow = 2)
```











<br/>
<br/>
<br/>
<br/>











```{r  warning=FALSE}
rawdata_list <- list.files("../",recursive = TRUE) %>% str_subset("_ALLDATA.csv") %>% str_subset("_Male_")
all_rawdata <- tibble()
for (rawdata_file in rawdata_list) {
  temp <- read_csv(paste0("../",rawdata_file),progress = FALSE)
  all_rawdata <- bind_rows(all_rawdata,temp)
}
```



```{r message=FALSE}
all_rawdata <- all_rawdata %>%
  full_join(x = all_rawdata, y = genotypes, by = c("FileName"="video","Id"="fly_id")) %>% 
  replace_na(list(genotype = "CS female"))
all_rawdata
```





<br/>
<br/>







```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Arena == 2) %>% 
  filter(Id == 3) %>% 
  filter(dist_to_other__mm > 2) %>% 
  ggplot(aes(x=Frame)) +
    geom_point(aes(y=wing_l_ang__rad, colour = "#F8766D")) +
    geom_point(aes(y=wing_r_ang__rad, colour = "#00BFC4"))
  
```


<br/>
<br/>





```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Arena == 2) %>% 
  filter(Id == 3) %>% 
  filter(dist_to_other__mm > 2) %>% 
  ggplot() +
    geom_density(aes(x=wing_l_ang__rad, colour = "#F8766D"),size = 2) +
    geom_density(aes(x=wing_r_ang__rad, colour = "#00BFC4"),size = 2)
```



<br/>
<br/>



```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Arena == 2) %>% 
  filter(Id == 3) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>% 
  ggplot() +
    geom_density(aes(x=wing_l_ang__rad, colour = "#F8766D"),size = 2) +
    geom_density(aes(x=wing_r_ang__rad, colour = "#00BFC4"),size = 2)
```








```{r}
# # Extract density data to average
# p <- ggplot_build(test_plot)
# ggplot(as.data.frame(p$data[[1]]), aes(x,y)) + geom_line()
# ggplot(as.data.frame(p$data[[2]]), aes(x,y)) + geom_line()
```



<br/>
<br/>




```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(genotype == "A") %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  ggplot(aes(x=max_wing_ang__rad,colour=individual)) +
    geom_density() + theme(legend.position = "none")
```




<br/>
<br/>



```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(genotype == "B") %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  ggplot(aes(x=max_wing_ang__rad,colour=individual)) +
    geom_density() + theme(legend.position = "none")
```




<br/>
<br/>



```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(genotype == "C") %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  ggplot(aes(x=max_wing_ang__rad,colour=individual)) +
    geom_density() + theme(legend.position = "none")
```



<br/>
<br/>



```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(genotype == "D") %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  ggplot(aes(x=max_wing_ang__rad,colour=individual)) +
    geom_density() + theme(legend.position = "none")
```



<br/>
<br/>



```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 3) %>% 
  ggplot(aes(x=Frame,y=angle_between__rad)) +
    geom_point()
```





<br/>
<br/>



```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Arena == 2) %>% 
  ggplot(aes(x=Frame,y=facing_angle__rad, color=as_factor(Id))) +
    geom_point()
```




<br/>
<br/>




```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Arena == 2) %>% 
  ggplot() +
    geom_density(aes(x=facing_angle__rad, color=as_factor(Id)),size = 2)
```





<br/>
<br/>



```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(genotype == "A") %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  ggplot(aes(x=facing_angle__rad,colour=individual)) +
    geom_density() + theme(legend.position = "none") +
    ylim(0,6)
```



```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(unique_fly == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_6_17") %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  ggplot(aes(x=facing_angle__rad,colour=unique_fly)) +
    geom_density() + theme(legend.position = "none") +
    ylim(0,6)
```

```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(unique_fly == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_6_17") %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  ggplot(aes(x=Frame,y=facing_angle__rad,colour=unique_fly)) +
    geom_point() + theme(legend.position = "none") #+
    #ylim(0,6)
```

```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(unique_fly == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_6_17") %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  ggplot(aes(x=Frame,y=dist_to_other__mm,colour=unique_fly)) +
    geom_point() + theme(legend.position = "none") #+
    #ylim(0,6)
```



```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(unique_fly == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_6_17") %>% 
  filter(SmoothedCopulation == 0) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  ggplot(aes(x=facing_angle__rad,colour=unique_fly)) +
    geom_density() + theme(legend.position = "none") +
    ylim(0,6)
```


```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(unique_fly == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_6_17") %>% 
  filter(Frame < 5000) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  ggplot(aes(x=facing_angle__rad,colour=unique_fly)) +
    geom_density() + theme(legend.position = "none") +
    ylim(0,6)
```

```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(unique_fly == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_6_17") %>% 
  ggplot(aes(x=Frame,y=dist_to_other__mm)) +
    geom_point(aes(colour=c(ifelse(SmoothedDistToOther>0.5,"not copulating","copulating"))))+
    geom_point(aes(y=c(ifelse(SmoothedCopulation==1,SmoothedCopulation+6,NA))))+
    geom_point(aes(y=c(ifelse(Copulation==1,Copulation+7,NA))))
```




```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(unique_fly == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_9_39") %>% 
  ggplot(aes(x=Frame,y=dist_to_other__mm)) +
    geom_point(aes(colour=c(ifelse(SmoothedDistToOther>0.5,"not copulating","copulating"))))+
    geom_point(aes(y=c(ifelse(SmoothedCopulation==1,SmoothedCopulation+6,NA))))+
    geom_point(aes(y=c(ifelse(Copulation==1,Copulation+7,NA))))
```









```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(genotype == "A") %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  summarise(id = unique(unique_fly),
            facing = mean(facing_angle__rad))  
```














```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(genotype == "B") %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  ggplot(aes(x=facing_angle__rad,colour=individual)) +
    geom_density() + theme(legend.position = "none") +
    ylim(0,6)
```

```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(genotype == "C") %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  ggplot(aes(x=facing_angle__rad,colour=individual)) +
    geom_density() + theme(legend.position = "none") +
    ylim(0,6)
```



<br/>
<br/>





```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(genotype == "D") %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  ggplot(aes(x=facing_angle__rad,colour=individual)) +
    geom_density() + theme(legend.position = "none") +
    ylim(0,6)
```



<br/>
<br/>





```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(genotype == "A") %>% 
  filter(dist_to_other__mm > 2) %>% 
  #filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  ggplot(aes(x=facing_angle__rad,colour=individual)) +
    geom_density() + theme(legend.position = "none") +
    ylim(0,6)
```




<br/>
<br/>



```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(genotype == "D") %>% 
  filter(dist_to_other__mm > 2) %>% 
  #filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  ggplot(aes(x=facing_angle__rad,colour=individual)) +
    geom_density() + theme(legend.position = "none") +
    ylim(0,6)
```




<br/>
<br/>




```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Arena == 2) %>% 
  ggplot(aes(x=Frame,y=dist_to_other__mm, color=dist_to_other__mm)) +
    geom_point() 
```


<br/>
<br/>





```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 3) %>% 
  ggplot(aes(x=Frame,y=dist_to_other__mm)) +
    geom_point(aes(colour=c(ifelse(dist_to_other__mm>2,"not copulating","copulating"))))
```





<br/>
<br/>


<br/>
<br/>






### D genotype distance to other

```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 27) %>% 
  ggplot(aes(x=Frame,y=dist_to_other__mm)) +
    geom_point(aes(colour=c(ifelse(dist_to_other__mm>2,"not copulating","copulating"))))
```



<br/>
<br/>




```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 29) %>% 
  ggplot(aes(x=Frame,y=dist_to_other__mm)) +
    geom_point(aes(colour=c(ifelse(dist_to_other__mm>2,"not copulating","copulating"))))
```


<br/>
<br/>




```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 35) %>% 
  ggplot(aes(x=Frame,y=dist_to_other__mm)) +
    geom_point(aes(colour=c(ifelse(dist_to_other__mm>2,"not copulating","copulating"))))
```



<br/>
<br/>




```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 37) %>% 
  ggplot(aes(x=Frame,y=dist_to_other__mm)) +
    geom_point(aes(colour=c(ifelse(dist_to_other__mm>2,"not copulating","copulating"))))
```





<br/>
<br/>





```{r}
all_rawdata <- all_rawdata %>%  
  unite("unique_fly",FileName,Id, remove = FALSE) %>% 
  group_by(unique_fly) %>% 
  mutate(
    Multitasking = (Approaching + Encircling + Contact + Turning + WingGesture),
    MultitaskingWithFacing = (Approaching + Encircling + Facing + Contact + Turning + WingGesture),
    Courtship = ifelse(Multitasking>=1, 1, 0),
    CourtshipWithFacing = ifelse(MultitaskingWithFacing>=1, 1, 0),
    MultitaskingWithCopulation = (Approaching + Encircling + Contact + Turning + WingGesture + Copulation),
    MultitaskingWithCopulationWithFacing = (Approaching + Encircling + Facing + Contact + Turning + WingGesture + Copulation),
    CourtshipAndCopulation = ifelse(MultitaskingWithCopulation>=1, 1, 0),
    CourtshipAndCopulationWthFacing = ifelse(MultitaskingWithCopulationWithFacing>=1, 1, 0),
    SmoothedCourtship = ifelse((rollmean(Courtship, 150, fill = c(0,0,0), align = c("left")))>0.5, 1, 0),
    SmoothedCopulation = ifelse((rollmean(Copulation, 1250, fill = c(0,0,0), align = c("center")))>0.5, 1, 0),
    SmoothedDistToOther = ifelse((rollmean(ifelse(dist_to_other__mm > 2, 1, 0), 250, fill = c(1,1,NA), align = c("center")))>0.5, 1, 0)
)
```






<br/>
<br/>






```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 37) %>% 
  ggplot(aes(x=Frame,y=dist_to_other__mm)) +
    geom_point(aes(colour=c(ifelse(SmoothedDistToOther==1,"not copulating","copulating"))))
```



<br/>
<br/>



```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 37) %>% 
  ggplot(aes(x=Frame)) +
    geom_point(aes(y=SmoothedDistToOther, colour = "#F8766D")) +
    geom_point(aes(y=(SmoothedCopulation+0.1), colour= "#00BFC4"))
    
```




<br/>
<br/>



```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 35) %>% 
  ggplot(aes(x=Frame,y=dist_to_other__mm)) +
    geom_point(aes(colour=c(ifelse(SmoothedDistToOther==1,"not copulating","copulating"))))
```





<br/>
<br/>




```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 35) %>% 
  ggplot(aes(x=Frame)) +
    geom_point(aes(y=SmoothedDistToOther, colour = "#F8766D")) +
    geom_point(aes(y=(SmoothedCopulation+0.1), colour= "#00BFC4"))
    
```




<br/>
<br/>





```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 35) %>% 
  ggplot(aes(x=Frame)) +
    geom_point(aes(y=SmoothedDistToOther, colour = "#F8766D")) +
    geom_point(aes(y=(SmoothedCopulation+0.1), colour= "#00BFC4")) +
    xlim(2000,3000)
```


<br/>
<br/>





```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 35) %>% 
  summarise(which.min(SmoothedDistToOther),
            which.max(SmoothedCopulation))
```








<br/>
<br/>









```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 3) %>% 
  ggplot(aes(x=Frame,y=dist_to_other__mm)) +
    geom_point(aes(colour=c(ifelse(SmoothedDistToOther==1,"not copulating","copulating"))))
```





<br/>
<br/>



```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 3) %>% 
  ggplot(aes(x=Frame,y=dist_to_other__mm)) +
    geom_point(aes(colour=c(ifelse(SmoothedCopulation==0,"not copulating","copulating"))))
```




<br/>
<br/>



```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 3) %>% 
  ggplot(aes(x=Frame)) +
    geom_point(aes(y=SmoothedDistToOther, colour = "#F8766D")) +
    geom_point(aes(y=(SmoothedCopulation+0.1), colour= "#00BFC4")) +
    xlim(6000,7000)
```



<br/>
<br/>




```{r warning=FALSE,  fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 3) %>% 
  summarise(which.min(SmoothedDistToOther),
            which.max(SmoothedCopulation))
```























<br/>
<br/>


<br/>
<br/>





### D genotype distance to other

```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 27) %>% 
  ggplot(aes(x=Frame,y=dist_to_other__mm)) +
    geom_point(aes(colour=c(ifelse(SmoothedDistToOther==1,"not copulating","copulating"))))
```



<br/>
<br/>




```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 29) %>% 
  ggplot(aes(x=Frame,y=dist_to_other__mm)) +
    geom_point(aes(colour=c(ifelse(SmoothedDistToOther==1,"not copulating","copulating"))))
```


<br/>
<br/>



```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 35) %>% 
  ggplot(aes(x=Frame,y=dist_to_other__mm)) +
    geom_point(aes(colour=c(ifelse(SmoothedDistToOther>0.5,"not copulating","copulating"))))
```



<br/>
<br/>




```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 37) %>% 
  ggplot(aes(x=Frame,y=dist_to_other__mm)) +
    geom_point(aes(colour=c(ifelse(SmoothedDistToOther==1,"not copulating","copulating"))))
```





















<br/>
<br/>


<br/>
<br/>









```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(genotype == "D") %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  ggplot(aes(x=max_wing_ang__rad,colour=individual)) +
    geom_density() + theme(legend.position = "none") +
    ylim(0,6) +
    xlim(0,pi)
```




<br/>
<br/>




```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(genotype == "A") %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  ggplot(aes(x=angle_between__rad,colour=individual)) +
    geom_density() + theme(legend.position = "none") +
    ylim(0,3) +
    xlim(0,pi)
```


<br/>
<br/>



```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(genotype == "D") %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  ggplot(aes(x=angle_between__rad,colour=individual)) +
    geom_density() + theme(legend.position = "none") +
    ylim(0,3) +
    xlim(0,pi)
```




<br/>
<br/>








### **Distance to wall**







```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(genotype == "A") %>% 
  filter(dist_to_other__mm > 2) %>% 
  #filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  ggplot(aes(x=dist_to_wall__mm,colour=individual)) +
    geom_density() + theme(legend.position = "none") +
    ylim(0,2.2) +
    xlim(0,10)
```





<br/>
<br/>




```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(genotype == "D") %>% 
  filter(dist_to_other__mm > 2) %>% 
  #filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  ggplot(aes(x=dist_to_wall__mm,colour=individual)) +
    geom_density() + theme(legend.position = "none") +
    ylim(0,2.2) +
    xlim(0,10)
```




<br/>
<br/>



```{r warning=FALSE, fig.width=16, fig.height=6}
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            mean_dist_to_wall_mm = mean(dist_to_wall__mm[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        (which.max(SmoothedCourtship)+(25*600))),
                                                                                  (which.max(SmoothedCourtship)+(25*600))
                                                                                  )
                                             ]
                                 )
            ) %>% 
  ggplot(aes(x=genotype,y=mean_dist_to_wall_mm)) +
    geom_boxplot()
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            median_dist_to_wall_mm = median(dist_to_wall__mm[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        (which.max(SmoothedCourtship)+(25*600))),
                                                                                  (which.max(SmoothedCourtship)+(25*600))
                                                                                  )
                                             ]
                                 )
            ) %>% 
  ggplot(aes(x=genotype,y=median_dist_to_wall_mm)) +
    geom_boxplot()
p3 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            prop_dist_to_wall_lt_1 = 100*sum(dist_to_wall__mm[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        (which.max(SmoothedCourtship)+(25*600))),
                                                                                  (which.max(SmoothedCourtship)+(25*600))
                                                                                  )
                                             ]<1,
                                 na.rm = TRUE)/
              length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        (which.max(SmoothedCourtship)+(25*600))),
                                                                                  (which.max(SmoothedCourtship)+(25*600))
                                                                                  )
                                             ]
                     )) %>% 
  ggplot(aes(x=genotype,y=prop_dist_to_wall_lt_1)) +
    geom_boxplot()
ggarrange(plotlist = list(p1,p2,p3),
          labels = c("Mean Distance to Wall",
                     "Median Distance to Wall",
                     "Prop Time Close to Wall"),
          hjust = -0.2,
          ncol = 3,
          nrow = 1)

```


















```{r warning=FALSE, fig.width=12, fig.height=6}
all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  filter(dist_to_other__mm > 2) %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  ggplot(aes(x=pos_x__px,y=pos_y__px)) +
    geom_point()
```






```{r}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 36) %>% 
  filter(dist_to_other__mm > 2) %>% 
  ggplot(aes(x=pos_x__px,y=pos_y__px)) +
    geom_point() +
    coord_fixed()
```





```{r}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 36) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_wall__mm > 1) %>% 
  ggplot(aes(x=pos_x__px,y=pos_y__px)) +
    geom_point() +
    coord_fixed()
```




```{r warning=FALSE, fig.width=12, fig.height=6}
p1 <- all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 36) %>% 
  #filter(dist_to_other__mm > 2) %>% 
  #filter(dist_to_wall__mm > 1) %>% 
  ggplot(aes(x=genotype,y=dist_to_wall__mm)) +
    geom_boxplot() +
  ylim(0,10)
p2 <- all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 36) %>% 
  filter(dist_to_other__mm > 2) %>% 
  #filter(dist_to_wall__mm > 1) %>% 
  ggplot(aes(x=genotype,y=dist_to_wall__mm)) +
    geom_boxplot() +
  ylim(0,10)
p3 <- all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 36) %>% 
  #filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_wall__mm > 1) %>% 
  ggplot(aes(x=genotype,y=dist_to_wall__mm)) +
    geom_boxplot() +
  ylim(0,10)
p4 <- all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 36) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_wall__mm > 1) %>% 
  ggplot(aes(x=genotype,y=dist_to_wall__mm)) +
    geom_boxplot() +
  ylim(0,10)
ggarrange(plotlist = list(p1,p2,p3,p4),
          labels = c("all frames",
                     "non cop frames",
                     "gt 1mm from wall",
                     "non cop, gt 1mm"),
          hjust = -0.4,
          ncol = 4,
          nrow = 1)

```


































<br/>
<br/>





```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(genotype == "A") %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  ggplot(aes(x=dist_to_other__mm,colour=individual)) +
    geom_density() + theme(legend.position = "none") +
    ylim(0,2) +
    xlim(0,20)
```




<br/>
<br/>




```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(genotype == "B") %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  ggplot(aes(x=dist_to_other__mm,colour=individual)) +
    geom_density() + theme(legend.position = "none") +
    ylim(0,2) +
    xlim(0,20)
```


<br/>
<br/>




```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(genotype == "C") %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  ggplot(aes(x=dist_to_other__mm,colour=individual)) +
    geom_density() + theme(legend.position = "none") +
    ylim(0,2) +
    xlim(0,20)
```





<br/>
<br/>




```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(genotype == "D") %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  ggplot(aes(x=dist_to_other__mm,colour=individual)) +
    geom_density() + theme(legend.position = "none") +
    ylim(0,2) +
    xlim(0,20)
```




<br/>
<br/>




```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  summarise(mean_dist_to_other = mean(dist_to_other__mm))


```



<br/>
<br/>


<br/>
<br/>



### **mean distance to other**

```{r warning=FALSE, fig.width=12, fig.height=4}
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  #filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(mean_dist_to_other = mean(dist_to_other__mm),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=mean_dist_to_other)) +
    geom_boxplot() +
    ylim(0,10)
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(mean_dist_to_other = mean(dist_to_other__mm),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=mean_dist_to_other)) +
    geom_boxplot() +
    ylim(0,10)
p3 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad < (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(mean_dist_to_other = mean(dist_to_other__mm),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=mean_dist_to_other)) +
    geom_boxplot() +
    ylim(0,10)
ggarrange(plotlist = list(p1,p2,p3),
          labels = c("All Frames","Wing Extension Frames","Non-Wing Extension Frames"),
          #hjust = 1,
          ncol = 3,
          nrow = 1)

```


<br/>
<br/>


<br/>
<br/>



### **median distance to other**

```{r warning=FALSE, fig.width=12, fig.height=4}
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  #filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(median_dist_to_other = median(dist_to_other__mm),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=median_dist_to_other)) +
    geom_boxplot() +
    ylim(0,10)
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(median_dist_to_other = median(dist_to_other__mm),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=median_dist_to_other)) +
    geom_boxplot() +
    ylim(0,10)
p3 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad < (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(median_dist_to_other = median(dist_to_other__mm),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=median_dist_to_other)) +
    geom_boxplot() +
    ylim(0,10)
ggarrange(plotlist = list(p1,p2,p3),
          labels = c("All Frames","Wing Extension Frames","Non-Wing Extension Frames"),
          hjust = -0.4,
          ncol = 3,
          nrow = 1)

```






<br/>
<br/>


<br/>
<br/>




### **mean facing angle** 


```{r warning=FALSE, fig.width=12, fig.height=4}
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  #filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(mean_facing_angle = mean(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=mean_facing_angle)) +
    geom_boxplot() +
    ylim(0,pi)
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(mean_facing_angle = mean(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=mean_facing_angle)) +
    geom_boxplot() +
    ylim(0,pi)
p3 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad < (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(mean_facing_angle = mean(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=mean_facing_angle)) +
    geom_boxplot() +
    ylim(0,pi)
ggarrange(plotlist = list(p1,p2,p3),
          labels = c("All Frames","Wing Extension Frames","Non-Wing Extension Frames"),
          #hjust = 1,
          ncol = 3,
          nrow = 1)

```










<br/>
<br/>


<br/>
<br/>




### **median facing angle**



```{r warning=FALSE, fig.width=12, fig.height=4}
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  #filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(median_facing_angle = median(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=median_facing_angle)) +
    geom_boxplot() +
    ylim(0,pi)
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(median_facing_angle = median(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=median_facing_angle)) +
    geom_boxplot() +
    ylim(0,pi)
p3 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad < (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(median_facing_angle = median(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=median_facing_angle)) +
    geom_boxplot() +
    ylim(0,pi)
ggarrange(plotlist = list(p1,p2,p3),
          labels = c("All Frames","Wing Extension Frames","Non-Wing Extension Frames"),
          #hjust = 1,
          ncol = 3,
          nrow = 1)

```



<br/>
<br/>



```{r warning=FALSE, fig.width=12, fig.height=4}
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  #filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(median_facing_angle = median(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=median_facing_angle)) +
    geom_boxplot() +
    ylim(0,1.5)
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(median_facing_angle = median(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=median_facing_angle)) +
    geom_boxplot() +
    ylim(0,1)
p3 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad < (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(median_facing_angle = median(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=median_facing_angle)) +
    geom_boxplot() +
    ylim(0,2)
ggarrange(plotlist = list(p1,p2,p3),
          labels = c("All Frames","Wing Extension Frames","Non-Wing Extension Frames"),
          #hjust = 1,
          ncol = 3,
          nrow = 1)

```



<br/>
<br/>



```{r}
test_stat_tibble0 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  #filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(median_facing_angle = median(facing_angle__rad),
            genotype = unique(genotype))
```

```{r}
aov0 <- aov(median_facing_angle~genotype,data = test_stat_tibble0)
summary(aov0)
```

```{r}
TukeyHSD(aov0)
```



<br/>
<br/>


<br/>
<br/>



### **median facing angle by sector**


```{r warning=FALSE, fig.width=12, fig.height=8}
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad < pi/6) %>% 
  #filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(median_facing_angle = median(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=median_facing_angle)) +
    geom_boxplot() +
    ylim(0,pi/6)
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad < pi/6) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(median_facing_angle = median(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=median_facing_angle)) +
    geom_boxplot() +
    ylim(0,pi/6)
p3 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad < pi/6) %>% 
  filter(max_wing_ang__rad < (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(median_facing_angle = median(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=median_facing_angle)) +
    geom_boxplot() +
    ylim(0,pi/6)
p4 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad > pi/6) %>% 
  #filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(median_facing_angle = median(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=median_facing_angle)) +
    geom_boxplot() +
    ylim(0,pi)
p5 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad > pi/6) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(median_facing_angle = median(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=median_facing_angle)) +
    geom_boxplot() +
    ylim(0,pi)
p6 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad > pi/6) %>% 
  filter(max_wing_ang__rad < (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(median_facing_angle = median(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=median_facing_angle)) +
    geom_boxplot() +
    ylim(0,pi)
ggarrange(plotlist = list(p1,p2,p3,p4,p5,p6),
          labels = c("All Frames","Wing Extension Frames","Non-Wing Extension Frames"),
          #hjust = 1,
          ncol = 3,
          nrow = 2)

```



<br/>
<br/>


<br/>
<br/>


### **mean facing angle by sector**

```{r warning=FALSE, fig.width=12, fig.height=8}
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad < pi/6) %>% 
  #filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(mean_facing_angle = mean(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=mean_facing_angle)) +
    geom_boxplot() +
    ylim(0,pi/6)
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad < pi/6) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(mean_facing_angle = mean(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=mean_facing_angle)) +
    geom_boxplot() +
    ylim(0,pi/6)
p3 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad < pi/6) %>% 
  filter(max_wing_ang__rad < (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(mean_facing_angle = mean(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=mean_facing_angle)) +
    geom_boxplot() +
    ylim(0,pi/6)
p4 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad > pi/6) %>% 
  #filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(mean_facing_angle = mean(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=mean_facing_angle)) +
    geom_boxplot() +
    ylim(0,pi)
p5 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad > pi/6) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(mean_facing_angle = mean(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=mean_facing_angle)) +
    geom_boxplot() +
    ylim(0,pi)
p6 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad > pi/6) %>% 
  filter(max_wing_ang__rad < (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(mean_facing_angle = mean(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=mean_facing_angle)) +
    geom_boxplot() +
    ylim(0,pi)
ggarrange(plotlist = list(p1,p2,p3,p4,p5,p6),
          labels = c("All Frames","Wing Extension Frames","Non-Wing Extension Frames"),
          #hjust = 1,
          ncol = 3,
          nrow = 2)

```




<br/>
<br/>


<br/>
<br/>



### **mean facing angle by wee sectors**

```{r warning=FALSE, fig.width=12, fig.height=8}
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad > 0) %>% 
  filter(facing_angle__rad <= pi/6) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(mean_facing_angle = mean(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=mean_facing_angle)) +
    geom_boxplot() #+
    #ylim(0,pi/6)
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad > pi/6) %>% 
  filter(facing_angle__rad <= 2*pi/6) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(mean_facing_angle = mean(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=mean_facing_angle)) +
    geom_boxplot() #+
    #ylim(0,pi/6)
p3 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad > 2*pi/6) %>% 
  filter(facing_angle__rad <= 3*pi/6) %>% 
  filter(max_wing_ang__rad < (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(mean_facing_angle = mean(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=mean_facing_angle)) +
    geom_boxplot() #+
    #ylim(0,pi/6)
p4 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad > 3*pi/6) %>% 
  filter(facing_angle__rad <= 4*pi/6) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(mean_facing_angle = mean(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=mean_facing_angle)) +
    geom_boxplot() #+
    #ylim(0,pi)
p5 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad > 4*pi/6) %>% 
  filter(facing_angle__rad <= 5*pi/6) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(mean_facing_angle = mean(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=mean_facing_angle)) +
    geom_boxplot() #+
    #ylim(0,pi)
p6 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad > 5*pi/6) %>% 
  filter(facing_angle__rad <= 6*pi/6) %>% 
  filter(max_wing_ang__rad < (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(mean_facing_angle = mean(facing_angle__rad),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=mean_facing_angle)) +
    geom_boxplot() #+
    #ylim(0,pi)
ggarrange(plotlist = list(p1,p2,p3,p4,p5,p6),
          labels = c("0-pi/6","pi/6-2pi/6","2pi/6-3pi/6",
                     "3pi/6-4pi/6","4pi/6-5pi/6","5pi/6-6pi/6"),
          #hjust = 1,
          ncol = 3,
          nrow = 2)

```








<br/>
<br/>


<br/>
<br/>






### **counts in wee sectors**

```{r warning=FALSE, fig.width=12, fig.height=8}
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad > 0) %>% 
  filter(facing_angle__rad <= pi/6) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(counts = length(Frame),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=counts)) +
    geom_boxplot() #+
    #ylim(0,pi/6)
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad > pi/6) %>% 
  filter(facing_angle__rad <= 2*pi/6) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(counts = length(Frame),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=counts)) +
    geom_boxplot() #+
    #ylim(0,pi/6)
p3 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad > 2*pi/6) %>% 
  filter(facing_angle__rad <= 3*pi/6) %>% 
  filter(max_wing_ang__rad < (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(counts = length(Frame),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=counts)) +
    geom_boxplot() #+
    #ylim(0,pi/6)
p4 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad > 3*pi/6) %>% 
  filter(facing_angle__rad <= 4*pi/6) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(counts = length(Frame),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=counts)) +
    geom_boxplot() #+
    #ylim(0,pi)
p5 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad > 4*pi/6) %>% 
  filter(facing_angle__rad <= 5*pi/6) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(counts = length(Frame),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=counts)) +
    geom_boxplot() #+
    #ylim(0,pi)
p6 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad > 5*pi/6) %>% 
  filter(facing_angle__rad <= 6*pi/6) %>% 
  filter(max_wing_ang__rad < (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(counts = length(Frame),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=counts)) +
    geom_boxplot() #+
    #ylim(0,pi)
ggarrange(plotlist = list(p1,p2,p3,p4,p5,p6),
          labels = c("0-pi/6","pi/6-2pi/6","2pi/6-3pi/6",
                     "3pi/6-4pi/6","4pi/6-5pi/6","5pi/6-6pi/6"),
          #hjust = 1,
          ncol = 3,
          nrow = 2)

```





<br/>
<br/>


<br/>
<br/>


#### **Total time in sector**


```{r warning=FALSE, fig.width=12, fig.height=8}
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad > 0) %>% 
  filter(facing_angle__rad <= pi/8) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(counts = length(Frame),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=counts)) +
    geom_boxplot() #+
    #ylim(0,pi/6)
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(facing_angle__rad > pi/8) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(counts = length(Frame),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=counts)) +
    geom_boxplot() #+
    #ylim(0,pi/6)
ggarrange(plotlist = list(p1,p2),
          labels = c("<pi/8",">pi/8"),
          hjust = -0.6,
          vjust = 2,
          ncol = 2,
          nrow = 1)

```



<br/>
<br/>


<br/>
<br/>


#### **Proportion time in sector**

```{r warning=FALSE, fig.width=12, fig.height=8}
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(counts = 100*sum(facing_angle__rad <= pi/8)/length(Frame),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=counts)) +
    geom_boxplot() #+
    #ylim(0,pi/6)
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(counts = 100*sum(facing_angle__rad > pi/8)/length(Frame),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=counts)) +
    geom_boxplot() #+
    #ylim(0,pi/6)
ggarrange(plotlist = list(p1,p2),
          labels = c("<pi/8",">pi/8"),
          hjust = -1.4,
          vjust = 2,
          ncol = 2,
          nrow = 1)

```


<br/>
<br/>


```{r}
test_stat_tibble0 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  group_by(unique_fly) %>% 
  summarise(counts = 100*sum(facing_angle__rad > pi/8)/length(Frame),
            genotype = unique(genotype))
aov0 <- aov(counts~genotype,data = test_stat_tibble0)
#summary(aov0)
TukeyHSD(aov0)
```



<br/>
<br/>



```{r}
pairwise.t.test(test_stat_tibble0$counts, test_stat_tibble0$genotype)
```













<br/>
<br/>





```{r warning=FALSE, fig.width=12, fig.height=12}
p1 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "A") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  geom_bin2d(bins = c(200,50)) +
  xlim(0,2*pi) +
  ylim(0,20) + 
  scale_fill_continuous(type = "viridis",limits = c(2,2000),na.value = "white") +
  coord_polar() +
  theme_void()

p2 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "B") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  geom_bin2d(bins = c(200,50)) +
  xlim(0,2*pi) +
  ylim(0,20) + 
  scale_fill_continuous(type = "viridis",limits = c(2,2000),na.value = "white") +
  coord_polar() +
  theme_void()

p3 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "C") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  geom_bin2d(bins = c(200,50)) +
  xlim(0,2*pi) +
  ylim(0,20) + 
  scale_fill_continuous(type = "viridis",limits = c(2,2000),na.value = "white") +
  coord_polar() +
  theme_void()

p4 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "D") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  geom_bin2d(bins = c(200,50)) +
  xlim(0,2*pi) +
  ylim(0,20) + 
  scale_fill_continuous(type = "viridis",limits = c(2,2000),na.value = "white") +
  coord_polar() +
  theme_void()

ggarrange(plotlist = list(p1,p2,p3,p4),
          labels = c("A","B","C","D"),
          #hjust = 1,
          ncol = 2,
          nrow = 2)

```


<br/>
<br/>


<br/>
<br/>


#### Total time in sector

```{r warning=FALSE, fig.width=12, fig.height=8}
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(facing_angle__rad > 0) %>% 
  filter(facing_angle__rad <= pi/8) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(counts = length(Frame),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=counts)) +
    geom_boxplot() #+
    #ylim(0,pi/6)
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(facing_angle__rad > pi/8) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(counts = length(Frame),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=counts)) +
    geom_boxplot() #+
    #ylim(0,pi/6)
ggarrange(plotlist = list(p1,p2),
          labels = c("<pi/8",">pi/8"),
          hjust = -0.6,
          vjust = 2,
          ncol = 2,
          nrow = 1)

```



<br/>
<br/>


<br/>
<br/>


#### Proportion time in sector and within 10mm

```{r warning=FALSE, fig.width=12, fig.height=8}
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(counts = 100*sum(facing_angle__rad <= pi/8)/length(Frame),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=counts)) +
    geom_boxplot() #+
    #ylim(0,pi/6)
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(counts = 100*sum(facing_angle__rad > pi/8)/length(Frame),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=counts)) +
    geom_boxplot() #+
    #ylim(0,pi/6)
ggarrange(plotlist = list(p1,p2),
          labels = c("<pi/8",">pi/8"),
          hjust = -1.4,
          vjust = 2,
          ncol = 2,
          nrow = 1)

```


<br/>
<br/>




```{r}
test_stat_tibble0 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  group_by(unique_fly) %>% 
  summarise(counts = 100*sum(facing_angle__rad > pi/8)/length(Frame),
            genotype = unique(genotype))
aov0 <- aov(counts~genotype,data = test_stat_tibble0)
#summary(aov0)
TukeyHSD(aov0)
```

<br/>
<br/>


```{r}
pairwise.t.test(test_stat_tibble0$counts, test_stat_tibble0$genotype)
```


<br/>
<br/>


<br/>
<br/>




```{r warning=FALSE, fig.width=12, fig.height=12}
p1 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "A") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  geom_bin2d(bins = c(200,50)) +
  xlim(0,2*pi) +
  ylim(0,10) + 
  scale_fill_continuous(type = "viridis",limits = c(0,2000),na.value = "white") +
  theme_void() +
  coord_polar()

p2 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "B") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  geom_bin2d(bins = c(200,50)) +
  xlim(0,2*pi) +
  ylim(0,10) + 
  scale_fill_continuous(type = "viridis",limits = c(0,2000),na.value = "white") +
  theme_void() +
  coord_polar()

p3 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "C") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  geom_bin2d(bins = c(200,50)) +
  xlim(0,2*pi) +
  ylim(0,10) + 
  scale_fill_continuous(type = "viridis",limits = c(0,2000),na.value = "white") +
  theme_void() +
  coord_polar()

p4 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "D") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  geom_bin2d(bins = c(200,50)) +
  xlim(0,2*pi) +
  ylim(0,10) + 
  scale_fill_continuous(type = "viridis",limits = c(0,2000),na.value = "white") +
  theme_void() +
  coord_polar()

ggarrange(plotlist = list(p1,p2,p3,p4),
          labels = c("A","B","C","D"),
          #hjust = 1,
          ncol = 2,
          nrow = 2)

```



<br/>
<br/>



```{r warning=FALSE, fig.width=12, fig.height=12}
p1 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "A") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  geom_bin2d(bins = c(200,50)) +
  xlim(0,2*pi) +
  ylim(0,10) + 
  scale_fill_continuous(type = "viridis",limits = c(4,2000),na.value = "white") +
  theme_void() +
  coord_polar()

p2 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "B") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  geom_bin2d(bins = c(200,50)) +
  xlim(0,2*pi) +
  ylim(0,10) + 
  scale_fill_continuous(type = "viridis",limits = c(4,2000),na.value = "white") +
  theme_void() +
  coord_polar()

p3 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "C") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  geom_bin2d(bins = c(200,50)) +
  xlim(0,2*pi) +
  ylim(0,10) + 
  scale_fill_continuous(type = "viridis",limits = c(4,2000),na.value = "white") +
  theme_void() +
  coord_polar()

p4 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "D") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  geom_bin2d(bins = c(200,50)) +
  xlim(0,2*pi) +
  ylim(0,10) + 
  scale_fill_continuous(type = "viridis",limits = c(4,2000),na.value = "white") +
  theme_void() +
  coord_polar()

ggarrange(plotlist = list(p1,p2,p3,p4),
          labels = c("A","B","C","D"),
          #hjust = 1,
          ncol = 2,
          nrow = 2)

```


<br/>
<br/>



```{r warning=FALSE, fig.width=12, fig.height=12}
p1 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "A") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  geom_bin2d(bins = c(40,10)) +
  xlim(0,2*pi) +
  ylim(0,10) + 
  scale_fill_continuous(type = "viridis",limits = c(50,50000),na.value = "white") +
  theme_void() +
  coord_polar()

p2 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "B") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  geom_bin2d(bins = c(40,10)) +
  xlim(0,2*pi) +
  ylim(0,10) + 
  scale_fill_continuous(type = "viridis",limits = c(50,50000),na.value = "white") +
  theme_void() +
  coord_polar()

p3 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "C") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  geom_bin2d(bins = c(40,10)) +
  xlim(0,2*pi) +
  ylim(0,10) + 
  scale_fill_continuous(type = "viridis",limits = c(50,50000),na.value = "white") +
  theme_void() +
  coord_polar()

p4 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "D") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  geom_bin2d(bins = c(40,10)) +
  xlim(0,2*pi) +
  ylim(0,10) + 
  scale_fill_continuous(type = "viridis",limits = c(50,50000),na.value = "white") +
  theme_void() +
  coord_polar()

ggarrange(plotlist = list(p1,p2,p3,p4),
          labels = c("A","B","C","D"),
          #hjust = 1,
          ncol = 2,
          nrow = 2)

```





<br/>
<br/>




```{r warning=FALSE, fig.width=12, fig.height=12}
p1 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "A") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  geom_bin2d(bins = c(100,25)) +
  xlim(0,2*pi) +
  ylim(0,10) + 
  scale_fill_continuous(type = "viridis",limits = c(10,2000),na.value = "white") +
  theme_void() +
  coord_polar()

p2 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "B") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  geom_bin2d(bins = c(100,25)) +
  xlim(0,2*pi) +
  ylim(0,10) + 
  scale_fill_continuous(type = "viridis",limits = c(10,2000),na.value = "white") +
  theme_void() +
  coord_polar()

p3 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "C") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  geom_bin2d(bins = c(100,25)) +
  xlim(0,2*pi) +
  ylim(0,10) + 
  scale_fill_continuous(type = "viridis",limits = c(10,2000),na.value = "white") +
  theme_void() +
  coord_polar()

p4 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "D") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  geom_bin2d(bins = c(100,25)) +
  xlim(0,2*pi) +
  ylim(0,10) + 
  scale_fill_continuous(type = "viridis",limits = c(10,2000),na.value = "white") +
  theme_void() +
  coord_polar()

ggarrange(plotlist = list(p1,p2,p3,p4),
          labels = c("A","B","C","D"),
          #hjust = 1,
          ncol = 2,
          nrow = 2)

```




















<br/>
<br/>








```{r warning=FALSE, fig.width=12, fig.height=12}
p1 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "A") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  stat_density_2d(aes(fill = ..level..), geom = "polygon", colour="white") +
  xlim(0,2*pi) +
  ylim(0,6) + 
  #scale_fill_continuous(type = "viridis",limits = c(2,2000),na.value = "white") +
  theme_grey() +
  coord_polar()
  

p2 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "B") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  stat_density_2d(aes(fill = ..level..), geom = "polygon", colour="white") +
  xlim(0,2*pi) +
  ylim(0,6) + 
  #scale_fill_continuous(type = "viridis",limits = c(2,2000),na.value = "white") +
  theme_grey() +
  coord_polar()

p3 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "C") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  stat_density_2d(aes(fill = ..level..), geom = "polygon", colour="white") +
  xlim(0,2*pi) +
  ylim(0,6) + 
  #scale_fill_continuous(type = "viridis",limits = c(2,2000),na.value = "white") +
  theme_grey() +
  coord_polar()

p4 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  filter(genotype == "D") %>% 
  ggplot(aes(x=facing_angle__rad, y=dist_to_other__mm)) +
  stat_density_2d(aes(fill = ..level..), geom = "polygon", colour="white") +
  xlim(0,2*pi) +
  ylim(0,6) + 
  #scale_fill_continuous(type = "viridis",limits = c(2,2000),na.value = "white") +
  theme_grey() +
  coord_polar()

ggarrange(plotlist = list(p1,p2,p3,p4),
          labels = c("A","B","C","D"),
          #hjust = 1,
          ncol = 2,
          nrow = 2)

```


<br/>
<br/>


<br/>
<br/>


#### Total count in inner ring



```{r warning=FALSE, fig.width=12, fig.height=6}
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(facing_angle__rad <= pi/8) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm <= 3) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(counts = length(Frame),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=counts)) +
    geom_boxplot() #+
    #ylim(0,pi/6)
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(facing_angle__rad <= pi/8) %>% 
  filter(dist_to_other__mm > 3) %>% 
  filter(dist_to_other__mm <= 10) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(counts = length(Frame),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=counts)) +
    geom_boxplot() #+
    #ylim(0,pi/6)
ggarrange(plotlist = list(p1,p2),
          labels = c("2mm-3mm","3mm-10mm"),
          hjust = -0.6,
          vjust = 2,
          ncol = 2,
          nrow = 1)

```




<br/>
<br/>


<br/>
<br/>


#### Proportion count in inner ring

```{r warning=FALSE, fig.width=12, fig.height=6}
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(facing_angle__rad <= pi/8) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(counts = 100*sum(dist_to_other__mm > 2 & dist_to_other__mm <= 3)/length(Frame),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=counts)) +
    geom_boxplot() #+
    #ylim(0,pi/6)
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  filter(facing_angle__rad <= pi/8) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  unite("individual", FileName:Id, remove = FALSE) %>% 
  group_by(individual) %>% 
  summarise(counts = 100*sum(dist_to_other__mm > 3 & dist_to_other__mm <= 10)/length(Frame),
            genotype = unique(genotype)) %>% 
  ggplot(aes(x=genotype, y=counts)) +
    geom_boxplot() #+
    #ylim(0,pi/6)
ggarrange(plotlist = list(p1,p2),
          labels = c("2mm-3mm","3mm-10mm"),
          hjust = -0.6,
          vjust = 2,
          ncol = 2,
          nrow = 1)

```




<br/>
<br/>


```{r}
test_stat_tibble0 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  filter(facing_angle__rad <= pi/8) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  group_by(unique_fly) %>% 
  summarise(counts = 100*sum(dist_to_other__mm > 2 & dist_to_other__mm <= 3)/length(Frame),
            genotype = unique(genotype))
aov0 <- aov(counts~genotype,data = test_stat_tibble0)
#summary(aov0)
TukeyHSD(aov0)
```

















<br/>
<br/>


<br/>
<br/>


<br/>
<br/>




# Left vs Right wing



```{r warning=FALSE, fig.width=12, fig.height=4}
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            wing_index = 100*sum(WingGesture[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        (which.max(SmoothedCourtship)+(25*600))),
                                                                                  (which.max(SmoothedCourtship)+(25*600))
                                                                                  )
                                             ]
                                 )/
              length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        (which.max(SmoothedCourtship)+(25*600))),
                                                                                  (which.max(SmoothedCourtship)+(25*600))
                                                                                  )
                                             ]
                     )) %>% 
  ggplot(aes(x=genotype,y=wing_index)) +
    geom_boxplot()
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            wing_index = 100*sum(max_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        (which.max(SmoothedCourtship)+(25*600))),
                                                                                  (which.max(SmoothedCourtship)+(25*600))
                                                                                  )
                                             ]>(35*pi/180),
                                 na.rm = TRUE)/
              length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        (which.max(SmoothedCourtship)+(25*600))),
                                                                                  (which.max(SmoothedCourtship)+(25*600))
                                                                                  )
                                             ]
                     )) %>%
  ggplot(aes(x=genotype,y=wing_index)) +
    geom_boxplot()
ggarrange(plotlist = list(p1,p2),
          labels = c("JAABA","wing angle"),
          hjust = -0.6,
          vjust = 2,
          ncol = 2,
          nrow = 1)

```




<br/>
<br/>




```{r warning=FALSE, fig.width=10, fig.height=8}
all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            Wing_index_by_JAABA = 100*sum(WingGesture[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]
                                 )/
              length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]
                     ),
            Wing_index_by_angle = 100*sum(max_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]>(35*pi/180),
                                 na.rm = TRUE)/
              length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]
                     )) %>%
  ggplot(aes(x=Wing_index_by_angle,y=Wing_index_by_JAABA,colour=genotype)) +
    geom_point() +
    geom_smooth(method=lm) +
    stat_cor(label.y = c(52,56,60,64), size =5) +
    stat_regline_equation(label.y = c(50,54,58,62), size =5) +
    coord_fixed()
```







<br/>
<br/>




```{r warning=FALSE, fig.width=10, fig.height=10}
wind <- 600
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            wing_index = 100*sum(max_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]>(35*pi/180),
                                 na.rm = TRUE)/
              length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]
                     )) %>% 
  ggplot(aes(x=genotype,y=wing_index)) +
    geom_boxplot()
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            left_wing_index = 100*sum(wing_l_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]<(-35*pi/180),
                                 na.rm = TRUE)/
              length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]
                     )) %>%
  ggplot(aes(x=genotype,y=left_wing_index)) +
    geom_boxplot()
p3 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            right_wing_index = 100*sum(wing_r_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]>(35*pi/180),
                                 na.rm = TRUE)/
              length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]
                     )) %>%
  ggplot(aes(x=genotype,y=right_wing_index)) +
    geom_boxplot()
p4 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            both_wing_index = 100*sum(min_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]>(35*pi/180),
                                 na.rm = TRUE)/
              length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]
                     )) %>%
  ggplot(aes(x=genotype,y=both_wing_index)) +
    geom_boxplot()
ggarrange(plotlist = list(p1,p4,p2,p3),
          labels = c("either wing","both wings","left wing","right wing"),
          hjust = -0.6,
          vjust = 2,
          ncol = 2,
          nrow = 2)
```


<br/>
<br/>



```{r}
test_stat_tibble <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  #filter(genotype != "A") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            both_wing_index = 100*sum(min_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]>(35*pi/180),
                                 na.rm = TRUE)/
              length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]
                     ))
aov1 <- aov(both_wing_index~genotype, data = test_stat_tibble)
TukeyHSD(aov1)
```


<br/>
<br/>


<br/>
<br/>



```{r warning=FALSE, fig.width=16, fig.height=4}
wind <- 600
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            both_wing_index = 100*sum(min_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]>(10*pi/180),
                                 na.rm = TRUE)/
              length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]
                     )) %>% 
  ggplot(aes(x=genotype,y=both_wing_index)) +
    geom_boxplot()
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            both_wing_index = 100*sum(min_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]>(20*pi/180),
                                 na.rm = TRUE)/
              length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]
                     )) %>%
  ggplot(aes(x=genotype,y=both_wing_index)) +
    geom_boxplot()
p3 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            both_wing_index = 100*sum(min_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]>(30*pi/180),
                                 na.rm = TRUE)/
              length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]
                     )) %>%
  ggplot(aes(x=genotype,y=both_wing_index)) +
    geom_boxplot()
p4 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            both_wing_index = 100*sum(min_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]>(40*pi/180),
                                 na.rm = TRUE)/
              length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]
                     )) %>%
  ggplot(aes(x=genotype,y=both_wing_index)) +
    geom_boxplot()
ggarrange(plotlist = list(p1,p2,p3,p4),
          labels = c(">10deg",">20deg",">30deg",">40deg"),
          hjust = -0.6,
          vjust = 2,
          ncol = 4,
          nrow = 1)
```


<br/>
<br/>









<br/>
<br/>



```{r}
test_stat_tibble <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  #filter(genotype != "A") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            both_wing_index = 100*sum(min_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]>(15*pi/180),
                                 na.rm = TRUE)/
              length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]
                     ))
aov1 <- aov(both_wing_index~genotype, data = test_stat_tibble)
TukeyHSD(aov1)
```



<br/>
<br/>


<br/>
<br/>



```{r warning=FALSE, fig.width=16, fig.height=4}
wind <- 600
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            mean_max_wing = mean(max_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ])
            ) %>% 
  ggplot(aes(x=genotype,y=mean_max_wing)) +
    geom_boxplot() +
    ylim(0,1.5)
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            mean_left_wing = -mean(wing_l_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ],na.rm = TRUE)
            ) %>%
  ggplot(aes(x=genotype,y=mean_left_wing)) +
    geom_boxplot() +
    ylim(0,1.5)
p3 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            mean_right_wing = mean(wing_r_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ],na.rm = TRUE)
            ) %>%
  ggplot(aes(x=genotype,y=mean_right_wing)) +
    geom_boxplot() +
    ylim(0,1.5)
p4 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            mean_min_wing = mean(min_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ])
            ) %>%
  ggplot(aes(x=genotype,y=mean_min_wing)) +
    geom_boxplot() +
    ylim(0,1.5)
ggarrange(plotlist = list(p1,p4,p2,p3),
          labels = c("max wing","min wings","left wing","right wing"),
          hjust = -0.6,
          vjust = 2,
          ncol = 4,
          nrow = 1)
```






<br/>
<br/>



```{r}
all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            mean_min_wing = mean(min_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ])
            ) %>%
  ggplot(aes(x=genotype,y=mean_min_wing)) +
    geom_boxplot() +
    ylim(0,.5)
```




<br/>
<br/>



```{r}
all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            min_wing_gt_15 = sum(mean(min_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ])>15*pi/180)
            ) %>% 
  summarise(tot_min_wing_gt_15 = sum(min_wing_gt_15>0,na.rm = TRUE),
            prop_min_wing_gt_15 = 100*sum(min_wing_gt_15>0,na.rm = TRUE)/length(min_wing_gt_15))
```











<br/>
<br/>


<br/>
<br/>






```{r warning=FALSE, fig.width=12, fig.height=12}
p1 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(min_wing_ang__rad > (15*pi/180)) %>%
  filter(genotype == "A") %>% 
  ggplot(aes(x=-wing_l_ang__rad, y=wing_l_len__px)) +
  geom_bin2d(bins = c(200,50)) +
  xlim(0,2*pi) +
  ylim(0,40) + 
  scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
  theme_void() +
  coord_polar()

p2 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(min_wing_ang__rad > (15*pi/180)) %>%
  filter(genotype == "B") %>% 
  ggplot(aes(x=-wing_l_ang__rad, y=wing_l_len__px)) +
  geom_bin2d(bins = c(200,50)) +
  xlim(0,2*pi) +
  ylim(0,40) + 
  scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
  theme_void() +
  coord_polar()

p3 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(min_wing_ang__rad > (15*pi/180)) %>%
  filter(genotype == "C") %>% 
  ggplot(aes(x=-wing_l_ang__rad, y=wing_l_len__px)) +
  geom_bin2d(bins = c(200,50)) +
  xlim(0,2*pi) +
  ylim(0,40) + 
  scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
  theme_void() +
  coord_polar()

p4 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(min_wing_ang__rad > (15*pi/180)) %>%
  filter(genotype == "D") %>% 
  ggplot(aes(x=-wing_l_ang__rad, y=wing_l_len__px)) +
  geom_bin2d(bins = c(200,50)) +
  xlim(0,2*pi) +
  ylim(0,40) + 
  scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
  theme_void() +
  coord_polar()

ggarrange(plotlist = list(p1,p2,p3,p4),
          labels = c("A","B","C","D"),
          #hjust = 1,
          ncol = 2,
          nrow = 2)

```

<br/>
<br/>


<br/>
<br/>



```{r warning=FALSE, fig.width=12, fig.height=12}
p1 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(min_wing_ang__rad > (15*pi/180)) %>%
  filter(genotype == "A") %>% 
  ggplot(aes(x=wing_r_ang__rad, y=wing_r_len__px)) +
  geom_bin2d(bins = c(200,50)) +
  xlim(0,2*pi) +
  ylim(0,40) + 
  scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
  theme_void() +
  coord_polar()

p2 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(min_wing_ang__rad > (15*pi/180)) %>%
  filter(genotype == "B") %>% 
  ggplot(aes(x=wing_r_ang__rad, y=wing_r_len__px)) +
  geom_bin2d(bins = c(200,50)) +
  xlim(0,2*pi) +
  ylim(0,40) + 
  scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
  theme_void() +
  coord_polar()

p3 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(min_wing_ang__rad > (15*pi/180)) %>%
  filter(genotype == "C") %>% 
  ggplot(aes(x=wing_r_ang__rad, y=wing_r_len__px)) +
  geom_bin2d(bins = c(200,50)) +
  xlim(0,2*pi) +
  ylim(0,40) + 
  scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
  theme_void() +
  coord_polar()

p4 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(min_wing_ang__rad > (15*pi/180)) %>%
  filter(genotype == "D") %>% 
  ggplot(aes(x=wing_r_ang__rad, y=wing_r_len__px)) +
  geom_bin2d(bins = c(200,50)) +
  xlim(0,2*pi) +
  ylim(0,40) + 
  scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
  theme_void() +
  coord_polar()

ggarrange(plotlist = list(p1,p2,p3,p4),
          labels = c("A","B","C","D"),
          #hjust = 1,
          ncol = 2,
          nrow = 2)

```











<br/>
<br/>


<br/>
<br/>




```{r warning=FALSE, fig.width=12, fig.height=12}
p1 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(min_wing_ang__rad > (15*pi/180)) %>%
  filter(genotype == "A") %>% 
  ggplot() +
  geom_bin2d(aes(x=wing_r_ang__rad, y=wing_r_len__px), bins = c(200,50)) +
  geom_bin2d(aes(x=wing_l_ang__rad, y=wing_l_len__px), bins = c(200,50)) +
  xlim(-pi,pi) +
  ylim(0,60) + 
  scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
  theme_void() +
  coord_polar()
p2 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(min_wing_ang__rad > (15*pi/180)) %>%
  filter(genotype == "B") %>% 
  ggplot() +
  geom_bin2d(aes(x=wing_r_ang__rad, y=wing_r_len__px), bins = c(200,50)) +
  geom_bin2d(aes(x=wing_l_ang__rad, y=wing_l_len__px), bins = c(200,50)) +
  xlim(-pi,pi) +
  ylim(0,60) + 
  scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
  theme_void() +
  coord_polar()
p3 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(min_wing_ang__rad > (15*pi/180)) %>%
  filter(genotype == "C") %>% 
  ggplot() +
  geom_bin2d(aes(x=wing_r_ang__rad, y=wing_r_len__px), bins = c(200,50)) +
  geom_bin2d(aes(x=wing_l_ang__rad, y=wing_l_len__px), bins = c(200,50)) +
  xlim(-pi,pi) +
  ylim(0,60) + 
  scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
  theme_void() +
  coord_polar()
p4 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(min_wing_ang__rad > (15*pi/180)) %>%
  filter(genotype == "D") %>% 
  ggplot() +
  geom_bin2d(aes(x=wing_r_ang__rad, y=wing_r_len__px), bins = c(200,50)) +
  geom_bin2d(aes(x=wing_l_ang__rad, y=wing_l_len__px), bins = c(200,50)) +
  xlim(-pi,pi) +
  ylim(0,60) + 
  scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
  theme_void() +
  coord_polar()

ggarrange(plotlist = list(p1,p2,p3,p4),
          labels = c("A","B","C","D"),
          #hjust = 1,
          ncol = 2,
          nrow = 2)
```













```{r warning=FALSE, fig.width=12, fig.height=12}
p1 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad < (2*pi/3)) %>%
  filter(min_wing_ang__rad > (0)) %>%
  filter(wing_l_ang__rad > (-2*pi/3)) %>%
  filter(wing_r_ang__rad < (2*pi/3)) %>%
  filter(wing_l_ang__rad < (0)) %>%
  filter(wing_r_ang__rad > (0)) %>%
  filter(dist_to_wall__mm > 2) %>%
  filter(min_wing_ang__rad > (15*pi/180)) %>%
  filter(genotype == "A") %>% 
  ggplot() +
  geom_bin2d(aes(x=wing_r_ang__rad, y=wing_r_len__px), bins = c(200,50)) +
  geom_bin2d(aes(x=wing_l_ang__rad, y=wing_l_len__px), bins = c(200,50)) +
  xlim(-pi,pi) +
  ylim(0,60) + 
  scale_fill_continuous(type = "viridis",limits = c(10,500),na.value = "white") +
  theme_void() +
  coord_polar()
p2 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad < (2*pi/3)) %>%
  filter(min_wing_ang__rad > (0)) %>%
  filter(wing_l_ang__rad > (-2*pi/3)) %>%
  filter(wing_r_ang__rad < (2*pi/3)) %>%
  filter(wing_l_ang__rad < (0)) %>%
  filter(wing_r_ang__rad > (0)) %>%
  filter(dist_to_wall__mm > 2) %>%
  filter(min_wing_ang__rad > (15*pi/180)) %>%
  filter(genotype == "B") %>% 
  ggplot() +
  geom_bin2d(aes(x=wing_r_ang__rad, y=wing_r_len__px), bins = c(200,50)) +
  geom_bin2d(aes(x=wing_l_ang__rad, y=wing_l_len__px), bins = c(200,50)) +
  xlim(-pi,pi) +
  ylim(0,60) + 
  scale_fill_continuous(type = "viridis",limits = c(10,500),na.value = "white") +
  theme_void() +
  coord_polar()
p3 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad < (2*pi/3)) %>%
  filter(min_wing_ang__rad > (0)) %>%
  filter(wing_l_ang__rad > (-2*pi/3)) %>%
  filter(wing_r_ang__rad < (2*pi/3)) %>%
  filter(wing_l_ang__rad < (0)) %>%
  filter(wing_r_ang__rad > (0)) %>%
  filter(dist_to_wall__mm > 2) %>%
  filter(min_wing_ang__rad > (15*pi/180)) %>%
  filter(genotype == "C") %>% 
  ggplot() +
  geom_bin2d(aes(x=wing_r_ang__rad, y=wing_r_len__px), bins = c(200,50)) +
  geom_bin2d(aes(x=wing_l_ang__rad, y=wing_l_len__px), bins = c(200,50)) +
  xlim(-pi,pi) +
  ylim(0,60) + 
  scale_fill_continuous(type = "viridis",limits = c(10,500),na.value = "white") +
  theme_void() +
  coord_polar()
p4 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad < (2*pi/3)) %>%
  filter(min_wing_ang__rad > (0)) %>%
  filter(wing_l_ang__rad > (-2*pi/3)) %>%
  filter(wing_r_ang__rad < (2*pi/3)) %>%
  filter(wing_l_ang__rad < (0)) %>%
  filter(wing_r_ang__rad > (0)) %>%
  filter(dist_to_wall__mm > 2) %>%
  filter(min_wing_ang__rad > (15*pi/180)) %>%
  filter(genotype == "D") %>% 
  ggplot() +
  geom_bin2d(aes(x=wing_r_ang__rad, y=wing_r_len__px), bins = c(200,50)) +
  geom_bin2d(aes(x=wing_l_ang__rad, y=wing_l_len__px), bins = c(200,50)) +
  xlim(-pi,pi) +
  ylim(0,60) + 
  scale_fill_continuous(type = "viridis",limits = c(10,500),na.value = "white") +
  theme_void() +
  coord_polar()

ggarrange(plotlist = list(p1,p2,p3,p4),
          labels = c("A","B","C","D"),
          #hjust = 1,
          ncol = 2,
          nrow = 2)
```











<br/>
<br/>


<br/>
<br/>




```{r warning=FALSE, fig.width=12, fig.height=12}
p1 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(min_wing_ang__rad > (15*pi/180)) %>%
  filter(genotype == "A") %>% 
  ggplot() +
  stat_density_2d(aes(x=wing_r_ang__rad, y=wing_r_len__px,fill = ..level..), geom = "polygon") +
  stat_density_2d(aes(x=wing_l_ang__rad, y=wing_l_len__px,fill = ..level..), geom = "polygon") +
  xlim(-pi,pi) +
  ylim(0,60) + 
  #scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
  theme_void() +
  coord_polar()
p2 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(min_wing_ang__rad > (15*pi/180)) %>%
  filter(genotype == "B") %>% 
  ggplot() +
  stat_density_2d(aes(x=wing_r_ang__rad, y=wing_r_len__px,fill = ..level..), geom = "polygon") +
  stat_density_2d(aes(x=wing_l_ang__rad, y=wing_l_len__px,fill = ..level..), geom = "polygon") +
  xlim(-pi,pi) +
  ylim(0,60) + 
  #scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
  theme_void() +
  coord_polar()
p3 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(min_wing_ang__rad > (15*pi/180)) %>%
  filter(genotype == "C") %>% 
  ggplot() +
  stat_density_2d(aes(x=wing_r_ang__rad, y=wing_r_len__px,fill = ..level..), geom = "polygon") +
  stat_density_2d(aes(x=wing_l_ang__rad, y=wing_l_len__px,fill = ..level..), geom = "polygon") +
  xlim(-pi,pi) +
  ylim(0,60) + 
  #scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
  theme_void() +
  coord_polar()
p4 <- all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(min_wing_ang__rad > (15*pi/180)) %>%
  filter(genotype == "D") %>% 
  ggplot() +
  stat_density_2d(aes(x=wing_r_ang__rad, y=wing_r_len__px,fill = ..level..), geom = "polygon") +
  stat_density_2d(aes(x=wing_l_ang__rad, y=wing_l_len__px,fill = ..level..), geom = "polygon") +
  xlim(-pi,pi) +
  ylim(0,60) + 
  #scale_fill_continuous(type = "viridis",limits = c(0,1000),na.value = "white") +
  theme_void() +
  coord_polar()

ggarrange(plotlist = list(p1,p2,p3,p4),
          labels = c("A","B","C","D"),
          #hjust = 1,
          ncol = 2,
          nrow = 2)
```
















































<br/>
<br/>


<br/>
<br/>







```{r}
all_rawdata %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(dist_to_other__mm < 10) %>% 
  filter(max_wing_ang__rad > (25*pi/180)) %>%
  #filter(genotype == "D") %>% 
  ggplot(aes(x=genotype, y=wing_l_len__px))+
  geom_boxplot()
```





<br/>
<br/>


<br/>
<br/>


<br/>
<br/>


# **Facing angle at start vs end of wing bout**


<br/>
<br/>




```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 36) %>%
  slice(which.max(SmoothedCourtship):which.max(SmoothedCopulation)) %>% 
  ggplot(aes(x=Frame)) +
    geom_point(aes(y=-wing_l_ang__rad, colour = "#F8766D")) +
    geom_point(aes(y=wing_r_ang__rad, colour = "#00BFC4")) +
    geom_point(aes(y=c(ifelse(max_wing_ang__rad>=(25*pi/180),1.6,-1)))) +
    geom_point(aes(y=c(ifelse(WingGesture==1,WingGesture+0.8,WingGesture-1)))) +
    #geom_point(aes(y=c(ifelse(WingGesture==1,WingGesture+0.6,WingGesture-1)))) +
  ylim(0,2)
```



<br/>
<br/>




```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_6") %>% 
  filter(Id == 7) %>%
  slice(which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                             ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)),
                                                    which.max(SmoothedCopulation),
                                                    min((which.max(SmoothedCourtship)+(25*600)),max(Frame))
                                                    ),
                                              min((which.max(SmoothedCourtship)+(25*600)),max(Frame))
                                              )) %>%
  ggplot(aes(x=Frame)) +
    geom_point(aes(y=-wing_l_ang__rad, colour = "#F8766D")) +
    geom_point(aes(y=wing_r_ang__rad, colour = "#00BFC4")) +
    geom_point(aes(y=c(ifelse(max_wing_ang__rad>=(35*pi/180),1.6,-1)))) +
    geom_point(aes(y=c(ifelse(WingGesture==1,WingGesture+0.8,WingGesture-1)))) +
    #geom_point(aes(y=c(ifelse(WingGesture==1,WingGesture+0.6,WingGesture-1)))) +
  ylim(0,2)
```



<br/>
<br/>


```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 5) %>%
  slice(which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                             ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)),
                                                    which.max(SmoothedCopulation),
                                                    min((which.max(SmoothedCourtship)+(25*600)),max(Frame))
                                                    ),
                                              min((which.max(SmoothedCourtship)+(25*600)),max(Frame))
                                              )) %>%
  ggplot(aes(x=Frame)) +
    geom_point(aes(y=-wing_l_ang__rad, colour = "#F8766D")) +
    geom_point(aes(y=wing_r_ang__rad, colour = "#00BFC4")) +
    geom_point(aes(y=c(ifelse(max_wing_ang__rad>=(35*pi/180),1.6,-1)))) +
    geom_point(aes(y=c(ifelse(WingGesture==1,WingGesture+0.8,WingGesture-1)))) +
    #geom_point(aes(y=c(ifelse(WingGesture==1,WingGesture+0.6,WingGesture-1)))) +
  ylim(0,2)
```










<br/>
<br/>


<br/>
<br/>







### **Facing angle at start and end of each bout**




```{r}
temp <- all_rawdata %>% 
  filter(genotype!="CS female") %>% 
  filter(dist_to_other__mm > 2)
  
uniq_fly <- unique(temp$unique_fly)
facing_start_and_end_wing <- tibble()
for (fly in uniq_fly) {
  temp2 <- filter(temp, unique_fly == fly)
  bouts <- rle(temp2$WingGesture)
  
  starts <- as.numeric()
  for (i in seq(1,length(which(bouts$values==1)),1)) {
    starts[i] = sum(bouts$lengths[1:(i*2)-1])+1
  }
  ends <- as.numeric()
  for (i in seq(1,length(which(bouts$values==1)),1)) {
    ends[i] = sum(bouts$lengths[1:(i*2)])
  }
  genotype = rep(unique(temp2$genotype),length(which(bouts$values==1)))
  uni_id = rep(unique(temp2$unique_fly),length(which(bouts$values==1)))
  
  temp_tibble <- tibble(unique_fly = uni_id,
                        genotype = genotype,
                        facing_at_start = temp2$facing_angle__rad[starts],
                        facing_at_end = temp2$facing_angle__rad[ends]
                        )
  facing_start_and_end_wing <- bind_rows(facing_start_and_end_wing,temp_tibble)
}
facing_start_and_end_wing
```



<br/>
<br/>



```{r warning=FALSE, fig.width=12, fig.height=6}
p1 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_start)) +
  geom_boxplot()
p2 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_end)) +
  geom_boxplot()
p3 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_start/facing_at_end)) +
  geom_boxplot() +
  ylim(0,2)
ggarrange(plotlist = list(p1,p2,p3),
          labels = c("Facing Angle at Start","Facing Angle at End","Ratio start:end"),
          hjust = -0.2,
          vjust = 1.6,
          ncol = 3,
          nrow = 1)

```



<br/>
<br/>




```{r warning=FALSE, fig.width=12, fig.height=6}
p1 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_start-facing_at_end)) +
  geom_boxplot()+
  ylim(-0.5,0.5)
p2 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_end-facing_at_start)) +
  geom_boxplot()+
  ylim(-0.5,0.5)
p3 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_start/facing_at_end)) +
  geom_boxplot() +
  ylim(0,2)
ggarrange(plotlist = list(p1,p2,p3),
          labels = c("Start-End","End-Start","Ratio start:end"),
          hjust = -0.2,
          vjust = 1.6,
          ncol = 3,
          nrow = 1)
```








<br/>
<br/>




```{r warning=FALSE, fig.width=12, fig.height=6}
p1 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_start)) +
  geom_violin()
p2 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_end)) +
  geom_violin()
ggarrange(plotlist = list(p1,p2),
          labels = c("Facing Angle at Start","Facing Angle at End"),
          #hjust = -0.6,
          #vjust = 2,
          ncol = 2,
          nrow = 1)

```




<br/>
<br/>


<br/>
<br/>


<br/>
<br/>


### **Mean facing angle at start and end of bout**


```{r}
temp <- all_rawdata %>% 
  filter(genotype!="CS female") %>% 
  filter(dist_to_other__mm > 2)
  
uniq_fly <- unique(temp$unique_fly)
facing_start_and_end_wing <- tibble()
for (fly in uniq_fly) {
  temp2 <- filter(temp, unique_fly == fly)
  bouts <- rle(temp2$WingGesture)
  
  starts <- as.numeric()
  for (i in seq(1,length(which(bouts$values==1)),1)) {
    starts[i] = sum(bouts$lengths[1:(i*2)-1])+1
  }
  ends <- as.numeric()
  for (i in seq(1,length(which(bouts$values==1)),1)) {
    ends[i] = sum(bouts$lengths[1:(i*2)])
  }
  genotype = unique(temp2$genotype)
  uni_id = unique(temp2$unique_fly)
  
  temp_tibble <- tibble(unique_fly = uni_id,
                        genotype = genotype,
                        facing_at_start = mean(temp2$facing_angle__rad[starts]),
                        facing_at_end = mean(temp2$facing_angle__rad[ends])
                        )
  facing_start_and_end_wing <- bind_rows(facing_start_and_end_wing,temp_tibble)
}
facing_start_and_end_wing
```




<br/>
<br/>



```{r warning=FALSE, fig.width=12, fig.height=6}
p1 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_start)) +
  geom_boxplot() +
  ylim(0,1)
p2 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_end)) +
  geom_boxplot() +
  ylim(0,1)
ggarrange(plotlist = list(p1,p2),
          labels = c("mean Facing Angle at Start","mean Facing Angle at End"),
          #hjust = -0.6,
          #vjust = 2,
          ncol = 2,
          nrow = 1)

```




<br/>
<br/>


<br/>
<br/>


### **Median facing angle at start and end of bout (using WingGesture)**


```{r}
temp <- all_rawdata %>% 
  filter(genotype!="CS female") %>% 
  filter(dist_to_other__mm > 2)
  
uniq_fly <- unique(temp$unique_fly)
facing_start_and_end_wing <- tibble()
for (fly in uniq_fly) {
  temp2 <- filter(temp, unique_fly == fly)
  bouts <- rle(temp2$WingGesture)
  
  starts <- as.numeric()
  for (i in seq(1,length(which(bouts$values==1)),1)) {
    starts[i] = sum(bouts$lengths[1:(i*2)-1])+1
  }
  ends <- as.numeric()
  for (i in seq(1,length(which(bouts$values==1)),1)) {
    ends[i] = sum(bouts$lengths[1:(i*2)])
  }
  genotype = unique(temp2$genotype)
  uni_id = unique(temp2$unique_fly)
  
  temp_tibble <- tibble(unique_fly = uni_id,
                        genotype = genotype,
                        facing_at_start = median(temp2$facing_angle__rad[starts]),
                        facing_at_end = median(temp2$facing_angle__rad[ends])
                        )
  facing_start_and_end_wing <- bind_rows(facing_start_and_end_wing,temp_tibble)
}
facing_start_and_end_wing
```




<br/>
<br/>



```{r warning=FALSE, fig.width=16, fig.height=8}
p1 <- all_rawdata %>% 
  filter(genotype!="CS female") %>% 
  group_by(unique_fly) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(WingGesture==1) %>% 
  summarise(genotype = unique(genotype),
            median_facing = median(facing_angle__rad)) %>% 
  ggplot(aes(x=genotype,y=median_facing)) +
    geom_boxplot() +
    ylim(0,0.5)
p2 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_start)) +
  geom_boxplot() +
  ylim(0,0.5)
p3 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_end)) +
  geom_boxplot() +
  ylim(0,0.5)
p4 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_start/facing_at_end)) +
  geom_boxplot() #+
  #ylim(0,0.5)
ggarrange(plotlist = list(p1,p2,p3,p4),
          labels = c("median Facing angle throughout","median at Start of each bout","median at End of each bout","ratio start:end"),
          hjust = -0.2,
          vjust = 2,
          ncol = 4,
          nrow = 1)
```


<br/>
<br/>


<br/>
<br/>



### **Median facing angle at start and end of bout (using >35deg)**


```{r}
temp <- all_rawdata %>% 
  filter(genotype!="CS female") %>% 
  filter(dist_to_other__mm > 2)
  
uniq_fly <- unique(temp$unique_fly)
facing_start_and_end_wing <- tibble()
for (fly in uniq_fly) {
  temp2 <- filter(temp, unique_fly == fly)
  #bouts <- rle(temp2$WingGesture)
  temp2$bin_max_wing <- ifelse(temp2$max_wing_ang__rad >= (35*pi/180),1,0)
  temp2$bin_max_wing <- temp2$bin_max_wing %>% replace_na(0)
  bouts <- rle(temp2$bin_max_wing)

  starts <- as.numeric()
  for (i in seq(1,length(which(bouts$values==1)),1)) {
    starts[i] = sum(bouts$lengths[1:(i*2)-1])+1
  }
  ends <- as.numeric()
  for (i in seq(1,length(which(bouts$values==1)),1)) {
    ends[i] = sum(bouts$lengths[1:(i*2)])
  }
  genotype = unique(temp2$genotype)
  uni_id = unique(temp2$unique_fly)
  
  temp_tibble <- tibble(unique_fly = uni_id,
                        genotype = genotype,
                        facing_at_start = median(temp2$facing_angle__rad[starts]),
                        facing_at_end = median(temp2$facing_angle__rad[ends])
                        )
  facing_start_and_end_wing <- bind_rows(facing_start_and_end_wing,temp_tibble)
}
facing_start_and_end_wing
```



<br/>
<br/>




```{r warning=FALSE, fig.width=16, fig.height=8}
p1 <- all_rawdata %>% 
  filter(genotype!="CS female") %>% 
  group_by(unique_fly) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  summarise(genotype = unique(genotype),
            median_facing = median(facing_angle__rad)) %>% 
  ggplot(aes(x=genotype,y=median_facing)) +
    geom_boxplot() +
    ylim(0,0.5)
p2 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_start)) +
  geom_boxplot() +
  ylim(0,0.5)
p3 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_end)) +
  geom_boxplot() +
  ylim(0,0.5)
p4 <- ggplot(facing_start_and_end_wing, aes(x=genotype,y=facing_at_start/facing_at_end)) +
  geom_boxplot() #+
  #ylim(0,0.5)
ggarrange(plotlist = list(p1,p2,p3,p4),
          labels = c("median Facing angle throughout","median at Start of each bout","median at End of each bout","ratio start:end"),
          hjust = -0.2,
          vjust = 2,
          ncol = 4,
          nrow = 1)

```








<br/>
<br/>


<br/>
<br/>






### **Facing angles with wing extended using WingGesture**

```{r warning=FALSE, fig.width=16, fig.height=6}
p1 <- all_rawdata %>% 
  filter(genotype!="CS female") %>% 
  group_by(unique_fly) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(WingGesture==1) %>% 
  ggplot(aes(x=genotype,y=facing_angle__rad)) +
    geom_boxplot() +
    ylim(0,1)
p2 <- all_rawdata %>% 
  filter(genotype!="CS female") %>% 
  group_by(unique_fly) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(WingGesture==1) %>% 
  summarise(genotype = unique(genotype),
            median_facing = median(facing_angle__rad)) %>% 
  ggplot(aes(x=genotype,y=median_facing)) +
    geom_boxplot() +
    ylim(0,1)
p3 <- all_rawdata %>% 
  filter(genotype!="CS female") %>% 
  group_by(unique_fly) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(WingGesture==1) %>% 
  summarise(genotype = unique(genotype),
            mean_facing = mean(facing_angle__rad)) %>% 
  ggplot(aes(x=genotype,y=mean_facing)) +
    geom_boxplot() +
    ylim(0,1)
ggarrange(plotlist = list(p1,p2,p3),
          labels = c("Facing Angle","median Facing Angle","mean Facing Angle"),
          #hjust = -0.6,
          #vjust = 2,
          ncol = 3,
          nrow = 1)
```



<br/>
<br/>


<br/>
<br/>




### **Facing angles with wing extended using max_wing>35**


```{r warning=FALSE, fig.width=16, fig.height=6}
p1 <- all_rawdata %>% 
  filter(genotype!="CS female") %>% 
  group_by(unique_fly) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  ggplot(aes(x=genotype,y=facing_angle__rad)) +
    geom_boxplot() +
    ylim(0,1)
p2 <- all_rawdata %>% 
  filter(genotype!="CS female") %>% 
  group_by(unique_fly) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  summarise(genotype = unique(genotype),
            median_facing = median(facing_angle__rad)) %>% 
  ggplot(aes(x=genotype,y=median_facing)) +
    geom_boxplot() +
    ylim(0,1)
p3 <- all_rawdata %>% 
  filter(genotype!="CS female") %>% 
  group_by(unique_fly) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  summarise(genotype = unique(genotype),
            mean_facing = mean(facing_angle__rad)) %>% 
  ggplot(aes(x=genotype,y=mean_facing)) +
    geom_boxplot() +
    ylim(0,1)
ggarrange(plotlist = list(p1,p2,p3),
          labels = c("Facing Angle","median Facing Angle","mean Facing Angle"),
          #hjust = -0.6,
          #vjust = 2,
          ncol = 3,
          nrow = 1)
```

  

<br/>
<br/>





```{r}
test_stat_tibble0 <- all_rawdata %>% 
  filter(genotype!="CS female") %>% 
  group_by(unique_fly) %>% 
  filter(dist_to_other__mm > 2) %>% 
  filter(max_wing_ang__rad > (35*pi/180)) %>%
  summarise(genotype = unique(genotype),
            facing = median(facing_angle__rad)) 
aov0 <- aov(facing~genotype,data = test_stat_tibble0)
#summary(aov0)
TukeyHSD(aov0)
```















<br/>
<br/>


<br/>
<br/>


<br/>
<br/>


<br/>
<br/>


<br/>
<br/>




```{r}
test <- all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 36) 
```


```{r}
temp2 <- test

#bouts <- rle(temp2$WingGesture)
temp2$bin_max_wing <- ifelse(temp2$max_wing_ang__rad > (35*pi/180),1,0)
temp2$bin_max_wing <- temp2$bin_max_wing %>% replace_na(0)
bouts <- rle(temp2$bin_max_wing)

starts <- as.numeric()
for (i in seq(1,length(which(bouts$values==1)),1)) {
  starts[i] = sum(bouts$lengths[1:(i*2)-1])+1
}
ends <- as.numeric()
for (i in seq(1,length(which(bouts$values==1)),1)) {
  ends[i] = sum(bouts$lengths[1:(i*2)])
}
genotype = rep(unique(temp2$genotype),length(which(bouts$values==1)))
uni_id = rep(unique(temp2$unique_fly),length(which(bouts$values==1)))

temp_tibble <- tibble(unique_fly = uni_id,
                      genotype = genotype,
                      facing_at_start = temp2$facing_angle__rad[starts],
                      facing_at_end = temp2$facing_angle__rad[ends]
                      )
temp_tibble

```


```{r}
starts = for (i in seq(1,length(which(bouts$values==1)),1)) {
                                           starts[i] = sum(bouts$lengths[1:(i*2)-1])+1
                                         }
starts
```




```{r}

test$bin_max_wing <- ifelse(test$max_wing_ang__rad > (35*pi/180),1,0)
test$bin_max_wing <- test$bin_max_wing %>% replace_na(0)
#test$bin_max_wing
bouts <- rle(test$bin_max_wing)
bouts
```


```{r}
bouts <- rle(test$WingGesture)
bouts
```



```{r warning=FALSE, fig.width=16, fig.height=4}
test <- all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 36) 

test$bin_max_wing <- ifelse(test$max_wing_ang__rad > (35*pi/180),1,0)
test$bin_max_wing <- test$bin_max_wing %>% replace_na(0)

test %>% 
  slice(which.max(SmoothedCourtship):which.max(SmoothedCopulation)) %>% 
  ggplot(aes(x=Frame)) +
    geom_point(aes(y=-wing_l_ang__rad, colour = "#F8766D")) +
    geom_point(aes(y=wing_r_ang__rad, colour = "#00BFC4")) +
    geom_point(aes(y=c(ifelse(bin_max_wing==1,1.6,-1)))) +
    geom_point(aes(y=c(ifelse(max_wing_ang__rad>=(35*pi/180),1.8,-1)))) +
    geom_point(aes(y=c(ifelse(WingGesture==1,WingGesture+1,WingGesture-1)))) +
    ylim(0,2.2)
```

```{r}
sum(test$bin_max_wing[which.max(test$SmoothedCourtship):which.max(test$SmoothedCopulation)]==test$WingGesture[which.max(test$SmoothedCourtship):which.max(test$SmoothedCopulation)])
sum(test$bin_max_wing[which.max(test$SmoothedCourtship):which.max(test$SmoothedCopulation)]!=test$WingGesture[which.max(test$SmoothedCourtship):which.max(test$SmoothedCopulation)])
```



```{r warning=FALSE, fig.width=16, fig.height=4}
test <- all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_123_3") %>% 
  filter(Id == 1) 

test$bin_max_wing <- ifelse(test$max_wing_ang__rad > (35*pi/180),1,0)
test$bin_max_wing <- test$bin_max_wing %>% replace_na(0)

test %>% 
  #slice(which.max(SmoothedCourtship):which.max(SmoothedCopulation)) %>% 
  ggplot(aes(x=Frame)) +
    geom_point(aes(y=-wing_l_ang__rad, colour = "#F8766D")) +
    geom_point(aes(y=wing_r_ang__rad, colour = "#00BFC4")) +
    geom_point(aes(y=c(ifelse(bin_max_wing==1,1.6,-1)))) +
    geom_point(aes(y=c(ifelse(max_wing_ang__rad>=(35*pi/180),1.8,-1)))) +
    geom_point(aes(y=c(ifelse(WingGesture==1,WingGesture+1,WingGesture-1)))) +
    ylim(0,2.2)
```

```{r}
gg_color_hue(4)
```


```{r warning=FALSE, fig.width=16, fig.height=6}
test <- all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_123_3") %>% 
  filter(Id == 1) %>% 
  filter(dist_to_other__mm > 2)

test$bin_max_wing <- ifelse(test$max_wing_ang__rad >= (35*pi/180),1,0)
test$bin_max_wing <- test$bin_max_wing %>% replace_na(0)

test %>% 
  ggplot(aes(x=Frame)) +
    geom_point(aes(y=dist_to_other__mm, colour = "#F8766D")) +
    geom_point(aes(y=SmoothedDistToOther-1, colour = "#7CAE00")) +
    geom_point(aes(y=(SmoothedCopulation-2), colour= "#00BFC4")) +

    geom_point(aes(y=c(ifelse(bin_max_wing==1,-3,-10)))) +
    geom_point(aes(y=c(ifelse(max_wing_ang__rad>=(35*pi/180),-4,-10)))) +
    geom_point(aes(y=c(ifelse(WingGesture==1,-6,-10)))) +
    geom_point(aes(y=c(ifelse(SmoothedCourtship==1,20,-10)))) +
    ylim(-6,20)
```

```{r}
max(test$Frame)
```



```{r}
temp2 <- all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_123_3") %>% 
  filter(Id == 1) %>% 
  filter(dist_to_other__mm > 2)

#bouts <- rle(temp2$WingGesture)
temp2$bin_max_wing <- ifelse(temp2$max_wing_ang__rad >= (35*pi/180),1,0)
temp2$bin_max_wing <- temp2$bin_max_wing %>% replace_na(0)
bouts <- rle(test$bin_max_wing)

starts <- as.numeric()
for (i in seq(1,length(which(bouts$values==1)),1)) {
  starts[i] = sum(bouts$lengths[1:(i*2)-1])+1
}
ends <- as.numeric()
for (i in seq(1,length(which(bouts$values==1)),1)) {
  ends[i] = sum(bouts$lengths[1:(i*2)])
}
genotype = unique(temp2$genotype)
uni_id = unique(temp2$unique_fly)

temp_tibble <- tibble(unique_fly = uni_id,
                      genotype = genotype,
                      facing_at_start = median(temp2$facing_angle__rad[starts]),
                      facing_at_end = median(temp2$facing_angle__rad[ends])
                      )
temp_tibble
```





```{r}
bouts <- rle(test$bin_max_wing)
bouts
```


































```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_123_3") %>% 
  filter(Id == 21) %>% 
  ggplot(aes(x=Frame)) +
    geom_point(aes(y=SmoothedCourtship, colour = "#F8766D")) +
    geom_point(aes(y=(SmoothedCopulation+0.1), colour= "#00BFC4")) #+
    #xlim(2000,3000)
```











```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 36) %>%
  #slice(which.max(SmoothedCourtship):which.max(SmoothedCopulation)) %>% 
  ggplot(aes(x=Frame)) +
    geom_point(aes(y=wing_l_ang__rad, colour = "#F8766D")) +
    geom_point(aes(y=wing_r_ang__rad, colour = "#00BFC4"))
  
```



```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 36) %>%
  slice(which.max(SmoothedCourtship):which.max(SmoothedCopulation)) %>% 
  ggplot(aes(x=Frame)) +
    geom_point(aes(y=-wing_l_ang__rad, colour = "#F8766D")) +
    geom_point(aes(y=wing_r_ang__rad, colour = "#00BFC4")) +
    geom_point(aes(y=c(ifelse(WingGesture==1,WingGesture+0.6,WingGesture-0.2))))
```

```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 36) %>%
  slice(which.max(SmoothedCourtship):which.max(SmoothedCopulation)) %>% 
  ggplot(aes(x=Frame)) +
    geom_point(aes(y=-wing_l_ang__rad, colour = "#F8766D")) +
    geom_point(aes(y=wing_r_ang__rad, colour = "#00BFC4")) +
    geom_point(aes(y=c(ifelse(WingGesture==1,WingGesture+0.6,NA))))
```



```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 36) %>% 
  ggplot(aes(x=Frame,y=dist_to_other__mm)) +
    geom_point(aes(colour=c(ifelse(SmoothedDistToOther>0.5,"not copulating","copulating"))))+
    geom_point(aes(y=c(ifelse(SmoothedCopulation==1,SmoothedCopulation+6,NA))))
```





```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 36) %>% 
  ggplot(aes(x=Frame)) +
    geom_point(aes(y=SmoothedDistToOther, colour = "#F8766D")) +
    geom_point(aes(y=(SmoothedCopulation+0.1), colour= "#00BFC4")) +
    xlim(2000,3000)
```



```{r warning=FALSE, fig.width=16, fig.height=4}
all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 36) %>% 
  ggplot(aes(x=Frame)) +
    geom_point(aes(y=SmoothedCourtship, colour = "#F8766D")) +
    geom_point(aes(y=(SmoothedCopulation+0.1), colour= "#00BFC4")) #+
    #xlim(2000,3000)
```












































































































































































































# **Trying to make functions for courtship window and calculate indices... they aren't working right, and seem to be ignoring the grouping of the tibble and returning the same value for every individual...**

If i have to for loop over the groups of a tibble, the below line might be usefull.
dim(unique(all_rawdata[all_rawdata %>% group_by(genotype) %>% group_vars()]))[1]






```{r}
courtship_window <- function(input,wind=600,...){
  temp_tibble <- summarise(input, start_of_courtship = which.max(SmoothedCourtship),
                                  end_of_courtship = ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                             ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                    which.max(SmoothedCopulation), 
                                                                    min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                    ),
                                                              min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                              )
                        )
  return(temp_tibble$start_of_courtship:temp_tibble$end_of_courtship)
  #return(c(temp_tibble$start_of_courtship,temp_tibble$end_of_courtship))
  #return(temp_tibble)
}
```


```{r}
courtship_window <- function(input,wind=600,...){
  temp_tibble <- summarise(input, start_of_courtship = which.max(SmoothedCourtship),
                                  end_of_courtship = ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                             ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                    which.max(SmoothedCopulation), 
                                                                    min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                    ),
                                                              min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                              )
                        )
  #return(temp_tibble$start_of_courtship:temp_tibble$end_of_courtship)
  return(c(temp_tibble$start_of_courtship,temp_tibble$end_of_courtship))
  #return(temp_tibble)
}
```








```{r}
courtship_window(test, wind = 600)
```





```{r}
temp <- all_rawdata %>% 
  filter(genotype!="CS female")

uniq_fly <- unique(temp$unique_fly)
courting_frames <- tibble()
for (fly in uniq_fly) {
  temp2 <- temp %>% filter(unique_fly == fly) 
  temp2 <- slice(temp2, which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                             ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*wind)), 
                                                                    which.max(SmoothedCopulation), 
                                                                    min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                    ),
                                                              min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                              ))
  courting_frames <- bind_rows(courting_frames,temp2)
}
courting_frames
```



```{r warning=FALSE, fig.width=16, fig.height=6}
test <- all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_123_3") %>% 
  filter(Id == 1) %>% 
  filter(dist_to_other__mm > 2)

#test$bin_max_wing <- ifelse(test$max_wing_ang__rad >= (35*pi/180),1,0)
#test$bin_max_wing <- test$bin_max_wing %>% replace_na(0)

test %>% 
  ggplot(aes(x=Frame)) +
    geom_point(aes(y=dist_to_other__mm, colour = "#F8766D")) +
    geom_point(aes(y=SmoothedDistToOther-1, colour = "#7CAE00")) +
    geom_point(aes(y=(SmoothedCopulation-2), colour= "#00BFC4")) +

    #geom_point(aes(y=c(ifelse(bin_max_wing==1,-3,-10)))) +
    geom_point(aes(y=c(ifelse(max_wing_ang__rad>=(35*pi/180),-4,-10)))) +
    geom_point(aes(y=c(ifelse(WingGesture==1,-6,-10)))) +
    geom_point(aes(y=c(ifelse(SmoothedCourtship==1,20,-10)))) +
    ylim(-6,20)
```

```{r warning=FALSE, fig.width=16, fig.height=6}
test <- courting_frames %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_123_3") %>% 
  filter(Id == 1) #%>% 
  #filter(dist_to_other__mm > 2)

#test$bin_max_wing <- ifelse(test$max_wing_ang__rad >= (35*pi/180),1,0)
#test$bin_max_wing <- test$bin_max_wing %>% replace_na(0)

test %>% 
  ggplot(aes(x=Frame)) +
    geom_point(aes(y=dist_to_other__mm, colour = "#F8766D")) +
    geom_point(aes(y=ifelse(SmoothedDistToOther==1,1,-10), colour = "#7CAE00")) +
    geom_point(aes(y=ifelse(SmoothedCopulation==0,-2,-10), colour= "#00BFC4")) +
    geom_point(aes(y=ifelse(Copulation==0,-1,-10), colour= "#00BFC4")) +

    #geom_point(aes(y=c(ifelse(bin_max_wing==1,-3,-10)))) +
    geom_point(aes(y=c(ifelse(max_wing_ang__rad>=(35*pi/180),-4,-10)))) +
    geom_point(aes(y=c(ifelse(WingGesture==1,-6,-10)))) +
    geom_point(aes(y=c(ifelse(SmoothedCourtship==1,20,-10)))) +
    geom_point(aes(y=c(ifelse(Courtship==1,18,-10)))) +
    ylim(-6,20)
```








```{r}

```


```{r}

```


```{r}

```

```{r}

```


```{r}

```








```{r}
calculate_indices <- function(input = .,feature,jaaba=TRUE,thresh=NULL,wind=600){
  if (feature %in% names(input)) {
    var_ind = paste0(feature,"_index")
    if (jaaba) {
      temp_tibble <- summarise(input, genotype = unique(genotype), 
                                      !!var_ind := 100*sum(input[[feature]][which.max(input[["SmoothedCourtship"]]):ifelse(which.max(input[["SmoothedCopulation"]]) > which.max(input[["SmoothedCourtship"]]),
                                                       ifelse(which.max(input[["SmoothedCopulation"]]) <= (which.max(input[["SmoothedCourtship"]])+(25*wind)), 
                                                              which.max(input[["SmoothedCopulation"]]), 
                                                              min((which.max(input[["SmoothedCourtship"]])+(25*wind)),max(input[["Frame"]]))
                                                              ),
                                                        min((which.max(input[["SmoothedCourtship"]])+(25*wind)),max(input[["Frame"]]))
                                                        )])/
                                                    length(input[["Frame"]][which.max(input[["SmoothedCourtship"]]):ifelse(which.max(input[["SmoothedCopulation"]]) > which.max(input[["SmoothedCourtship"]]),
                                                       ifelse(which.max(input[["SmoothedCopulation"]]) <= (which.max(input[["SmoothedCourtship"]])+(25*wind)), 
                                                              which.max(input[["SmoothedCopulation"]]), 
                                                              min((which.max(input[["SmoothedCourtship"]])+(25*wind)),max(input[["Frame"]]))
                                                              ),
                                                        min((which.max(input[["SmoothedCourtship"]])+(25*wind)),max(input[["Frame"]]))
                                                        )])
                )
    } else {
      temp_tibble <- summarise(input, genotype = unique(genotype), 
                                      !!var_ind := 100*sum(input[[feature]][which.max(input[["SmoothedCourtship"]]):ifelse(which.max(input[["SmoothedCopulation"]]) > which.max(input[["SmoothedCourtship"]]),
                                                       ifelse(which.max(input[["SmoothedCopulation"]]) <= (which.max(input[["SmoothedCourtship"]])+(25*wind)), 
                                                              which.max(input[["SmoothedCopulation"]]), 
                                                              min((which.max(input[["SmoothedCourtship"]])+(25*wind)),max(input[["Frame"]]))
                                                              ),
                                                        min((which.max(input[["SmoothedCourtship"]])+(25*wind)),max(input[["Frame"]]))
                                                        )]>thresh,na.rm = TRUE)/
                                                    length(input[["Frame"]][which.max(input[["SmoothedCourtship"]]):ifelse(which.max(input[["SmoothedCopulation"]]) > which.max(input[["SmoothedCourtship"]]),
                                                       ifelse(which.max(input[["SmoothedCopulation"]]) <= (which.max(input[["SmoothedCourtship"]])+(25*wind)), 
                                                              which.max(input[["SmoothedCopulation"]]), 
                                                              min((which.max(input[["SmoothedCourtship"]])+(25*wind)),max(input[["Frame"]]))
                                                              ),
                                                        min((which.max(input[["SmoothedCourtship"]])+(25*wind)),max(input[["Frame"]]))
                                                        )])
                )
    }
    return(temp_tibble)
  } else {
    message(paste0(feature, " does not exist in table"))
  }
} 
```






```{r}
calculate_indices <- function(input,feature,jaaba=TRUE,thresh=NULL){
  if (feature %in% names(input)) {
    var_ind = paste0(feature,"_index")
    if (jaaba) {
      temp_tibble <- summarise(input, genotype = unique(genotype), 
                                      !!var_ind := 100*sum(input[[feature]][courtship_window(input)])/
                                                    length(input[["Frame"]][courtship_window(input)])
                )
    } else {
      temp_tibble <- summarise(input, genotype = unique(genotype), 
                                      !!var_ind := 100*sum(input[[feature]][courtship_window(input)]>thresh,na.rm = TRUE)/
                                                    length(input[["Frame"]][courtship_window(input)])
                )
    }
    return(temp_tibble)
  } else {
    message(paste0(feature, " does not exist in table"))
  }
} 
```





```{r warning=FALSE, fig.width=12, fig.height=4}
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(unique_fly) %>% 
  calculate_indices(feature = "max_wing_ang__rad",jaaba = FALSE,thresh = (25*pi/180)) %>% 
  ggplot(aes(x=genotype,y=max_wing_ang__rad_index)) +
    geom_boxplot()
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(unique_fly) %>% 
  calculate_indices(feature = "wing_l_ang__rad",jaaba = FALSE,thresh = (25*pi/180)) %>% 
  ggplot(aes(x=genotype,y=wing_l_ang__rad_index)) +
    geom_boxplot()
p3 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(unique_fly) %>% 
  calculate_indices(feature = "wing_r_ang__rad",jaaba = FALSE,thresh = (25*pi/180)) %>% 
  ggplot(aes(x=genotype,y=wing_r_ang__rad_index)) +
    geom_boxplot()
ggarrange(plotlist = list(p1,p2,p3),
          labels = c("wing index","left wing","right wing"),
          hjust = -0.6,
          vjust = 2,
          ncol = 3,
          nrow = 1)

```





```{r warning=FALSE, fig.width=12, fig.height=4}
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            wing_index = 100*sum(max_wing_ang__rad[courtship_window(input)]>(40*pi/180),
                                 na.rm = TRUE)/
              length(Frame[courtship_window(input)]
                     )) %>% 
  ggplot(aes(x=genotype,y=wing_index)) +
    geom_boxplot()
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            left_wing_index = 100*sum(wing_l_ang__rad[courtship_window(input)]<(-40*pi/180),
                                 na.rm = TRUE)/
              length(Frame[courtship_window(input)]
                     )) %>%
  ggplot(aes(x=genotype,y=left_wing_index)) +
    geom_boxplot()
p3 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            right_wing_index = 100*sum(wing_r_ang__rad[courtship_window(input)]>(40*pi/180),
                                 na.rm = TRUE)/
              length(Frame[courtship_window(input)]
                     )) %>%
  ggplot(aes(x=genotype,y=right_wing_index)) +
    geom_boxplot()
ggarrange(plotlist = list(p1,p2,p3),
          labels = c("wing index","left wing","right wing"),
          hjust = -0.6,
          vjust = 2,
          ncol = 3,
          nrow = 1)

```








```{r warning=FALSE, fig.width=12, fig.height=4}
p1 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            wing_index = 100*sum(max_wing_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]>(40*pi/180),
                                 na.rm = TRUE)/
              length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]
                     )) %>% 
  ggplot(aes(x=genotype,y=wing_index)) +
    geom_boxplot()
p2 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            left_wing_index = 100*sum(wing_l_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]<(-40*pi/180),
                                 na.rm = TRUE)/
              length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]
                     )) %>%
  ggplot(aes(x=genotype,y=left_wing_index)) +
    geom_boxplot()
p3 <- all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  summarise(genotype = unique(genotype),
            right_wing_index = 100*sum(wing_r_ang__rad[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]>(40*pi/180),
                                 na.rm = TRUE)/
              length(Frame[which.max(SmoothedCourtship):ifelse(which.max(SmoothedCopulation) > which.max(SmoothedCourtship),
                                                                                 ifelse(which.max(SmoothedCopulation) <= (which.max(SmoothedCourtship)+(25*600)), 
                                                                                        which.max(SmoothedCopulation), 
                                                                                        min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                        ),
                                                                                  min((which.max(SmoothedCourtship)+(25*wind)),max(Frame))
                                                                                  )
                                             ]
                     )) %>%
  ggplot(aes(x=genotype,y=right_wing_index)) +
    geom_boxplot()
ggarrange(plotlist = list(p1,p2,p3),
          labels = c("wing index","left wing","right wing"),
          hjust = -0.6,
          vjust = 2,
          ncol = 3,
          nrow = 1)
```










```{r}
all_rawdata %>% 
  filter(genotype != "CS female") %>% 
  filter(genotype == "D") %>% 
  group_by(genotype) %>% 
  group_by(unique_fly) %>% 
  calculate_indices(feature = "max_wing_ang__rad",jaaba = FALSE,thresh = (25*pi/180))
```






```{r}
test <- all_rawdata %>% 
  filter(FileName == "Megan-2019_03_06_Courtship-DsxVglutTNT_Male_1234_2") %>% 
  filter(Id == 36) 
```


```{r}
calculate_indices(input = test,feature = "WingGesture",jaaba = TRUE)
```



```{r}
calculate_indices(input = test,feature = "max_wing_ang__rad",jaaba = FALSE,thresh = (25*pi/180))
```




```{r}
calculate_indices(input = test,feature = "max_wing_angle__rad",jaaba = FALSE,thresh = (25*pi/180))
```




























